AI Grammar Rephrase Online Free

AI Grammar Rephrase Online Free — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Index locking

In databases an index is a data structure, part of the database, used by a database system to efficiently navigate access to user data. Index data are system data distinct from user data, and consist primarily of pointers. Changes in a database (by insert, delete, or modify operations), may require indexes to be updated to maintain accurate user data accesses. Index locking is a technique used to maintain index integrity. A portion of an index is locked during a database transaction when this portion is being accessed by the transaction as a result of attempt to access related user data. Additionally, special database system transactions (not user-invoked transactions) may be invoked to maintain and modify an index, as part of a system's self-maintenance activities. When a portion of an index is locked by a transaction, other transactions may be blocked from accessing this index portion (blocked from modifying, and even from reading it, depending on lock type and needed operation). Index Locking Protocol guarantees that phantom read phenomenon won't occur. Index locking protocol states: Every relation must have at least one index. A transaction can access tuples only after finding them through one or more indices on the relation A transaction Ti that performs a lookup must lock all the index leaf nodes that it accesses, in S-mode, even if the leaf node does not contain any tuple satisfying the index lookup (e.g. for a range query, no tuple in a leaf is in the range) A transaction Ti that inserts, updates or deletes a tuple ti in a relation r must update all indices to r and it must obtain exclusive locks on all index leaf nodes affected by the insert/update/delete The rules of the two-phase locking protocol must be observed. Specialized concurrency control techniques exist for accessing indexes. These techniques depend on the index type, and take advantage of its structure. They are typically much more effective than applying to indexes common concurrency control methods applied to user data. Notable and widely researched are specialized techniques for B-trees (B-Tree concurrency control) which are regularly used as database indexes. Index locks are used to coordinate threads accessing indexes concurrently, and typically shorter-lived than the common transaction locks on user data. In professional literature, they are often called latches.
Read more →
Social media mining

Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services. Social media mining uses concepts from computer science, data mining, machine learning, and statistics. Mining is based on social network analysis, network science, sociology, ethnography, optimization and mathematics. It attempts to formally represent, measure and model patterns from social media data. In the 2010s, major corporations, governments and not-for-profit organizations began mining to learn about customers, clients and others. Platforms such as Google, Facebook (partnered with Datalogix and BlueKai) conduct mining to target users with advertising. Scientists and machine learning researchers extract insights and design product features. Users may not understand how platforms use their data. Users tend to click through Terms of Use agreements without reading them, leading to ethical questions about whether platforms adequately protect users' privacy. During the 2016 United States presidential election, Facebook allowed Cambridge Analytica, a political consulting firm linked to the Trump campaign, to analyze the data of an estimated 87 million Facebook users to profile voters, creating controversy when this was revealed. == Background == As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Instagram, Photobucket, or Picasa), news aggregation (Google Reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegrees.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media. == Uses == Social media mining is used across several industries including business development, social science research, health services, and educational purposes. Once the data received goes through social media analytics, it can then be applied to these various fields. Often, companies use the patterns of connectivity that pervade social networks, such as assortativity—the social similarity between users that are induced by influence, homophily, and reciprocity and transitivity. These forces are then measured via statistical analysis of the nodes and connections between these nodes. Social analytics also uses sentiment analysis, because social media users often relay positive or negative sentiment in their posts. This provides important social information about users' emotions on specific topics. These three patterns have several uses beyond pure analysis. For example, influence can be used to determine the most influential user in a particular network. Companies would be interested in this information in order to decide who they may hire for influencer marketing. These influencers are determined by recognition, activity generation, and novelty—three requirements that can be measured through the data mined from these sites. Analysts also value measures of homophily: the tendency of two similar individuals to become friends. Users have begun to rely on information of other users' opinions in order to understand diverse subject matter. These analyses can also help create recommendations for individuals in a tailored capacity. By measuring influence and homophily, online and offline companies are able to suggest specific products for individuals consumers, and groups of consumers. Social media networks can use this information themselves to suggest to their users possible friends to add, pages to follow, and accounts to interact with. == Perception == Modern social media mining is a controversial practice that has led to exponential gains in user growth for tech giants such as Facebook, Inc., Twitter, and Google. Companies such as these, considered "Big Tech" are companies that build algorithms that take advantage of user input to understand their preferences, and keep them on the platform as much as possible. These inputs, that can be as simple as time spent on a given screen, provide the data being mined, and lead to companies profiting heavily from using that data to capitalize on extremely accurate predictions about user behavior. The growth of platforms accelerated rapidly once these strategies were put in place; Most of the largest platforms now average over 1 billion active users per month as of 2021. It has been claimed by a multitude of anti-algorithm personalities, like Tristan Harris or Chamath Palihapitiya, that certain companies (specifically Facebook) valued growth above all else, and ignored potential negative impacts from these growth engineering tactics. At the same time, users have now created their own data arbitrages with the help of their own data, through content monetization and becoming influencers. Users typically have access to a varied set of analytics specific to people that interact with them on social media, and can use these as building blocks for their own targeting and growth strategies through ads and posts that cater to their audiences. Influencers also commonly promote products and services for established brands, creating one of the largest digital industries: Influencer marketing. Instagram, Facebook, Twitter, YouTube, Google, and others have long given access to platform analytics, and allowed third parties to access that information as well, at times unbeknownst to even the user whose data is being viewed/bought. == Research == === Research areas === Social media event detection – Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. Public health monitoring and surveillance - Using large-scale analysis of social media to study large cohorts of patients and the general public, e.g. to obtain early warning signals of drug-drug interactions and adverse drug reactions, or understand human reproduction and sexual interest. Community structure (Community Detection/Evolution/Evaluation) – Identifying communities on social networks, how t
Read more →
Data product

In data management and product management, a data product is a reusable, active, and standardized data asset designed to deliver measurable value to its users, whether internal or external, by applying the rigorous principles of product thinking and management. It comprises one or more data artifacts (e.g., datasets, models, pipelines) and is enriched with metadata, including governance policies, data quality rules, data contracts, and, where applicable, a software bill of materials (SBOM) to document its dependencies and components. Ownership of a data product is aligned to a specific domain or use case, ensuring accountability, stewardship, and its continuous evolution throughout its lifecycle. Adhering to the FAIR principles – findable, accessible, interoperable, and reusable – a data product is designed to be discoverable, scalable, reusable, and aligned with both business and regulatory standards, driving innovation and efficiency in modern data ecosystems. == History == In 2012, DJ Patil proposed the first documented definition: a data product is a product that facilitates an end goal through the use of data. In 2019, Zhamak Dehghani introduced Data Mesh, with a strong focus on domain-oriented data products. Later, in 2020, she solidifies Data Mesh around four principles, one being Data as a Product, in which she defines Data Product as the node on the mesh that encapsulates three structural components required for its function, providing access to the domain's analytical data as a product. In 2024, Andrea Gioia published one of the first books specifically on data products post Data Mesh announcement. In his book, Gioia defines the concept of pure data product. In 2025, during the Data Day Texas conference, Jean-Georges Perrin and a collective of product managers and data engineers got together to craft the current definition and make it available to the public domain. In July 2025, Bitol, a project of The Linux Foundation, released and early version of the Open Data Product Standard (ODPS) aiming at normalizing data products
Read more →
Data Reference Model

The Data Reference Model (DRM) is one of the five reference models of the Federal Enterprise Architecture. == Overview == The DRM is a framework whose primary purpose is to enable information sharing and reuse across the United States federal government via the standard description and discovery of common data and the promotion of uniform data management practices. The DRM describes artifacts which can be generated from the data architectures of federal government agencies. The DRM provides a flexible and standards-based approach to accomplish its purpose. The scope of the DRM is broad, as it may be applied within a single agency, within a community of interest, or cross-community of interest. == Data Reference Model topics == === DRM structure === The DRM provides a standard means by which data may be described, categorized, and shared. These are reflected within each of the DRM's three standardization areas: Data Description: Provides a means to uniformly describe data, thereby supporting its discovery and sharing. Data Context: Facilitates discovery of data through an approach to the categorization of data according to taxonomies. Additionally, enables the definition of authoritative data assets within a community of interest. Data Sharing: Supports the access and exchange of data where access consists of ad hoc requests (such as a query of a data asset), and exchange consists of fixed, re-occurring transactions between parties. Enabled by capabilities provided by both the Data Context and Data Description standardization areas. === DRM Version 2 === The Data Reference Model version 2 released in November 2005 is a 114-page document with detailed architectural diagrams and an extensive glossary of terms. The DRM also make many references to ISO standards specifically the ISO/IEC 11179 metadata registry standard. === DRM usage === The DRM is not technically a published technical interoperability standard such as web services, it is an excellent starting point for data architects within federal and state agencies. Any federal or state agencies that are involved with exchanging information with other agencies or that are involved in data warehousing efforts should use this document as a guide.
Read more →
Virtual intelligence

Virtual intelligence (VI) is the term given to artificial intelligence that exists within a virtual world. Many virtual worlds have options for persistent avatars that provide information, training, role-playing, and social interactions. The immersion in virtual worlds provides a platform for VI beyond the traditional paradigm of past user interfaces (UIs). What Alan Turing established as a benchmark for telling the difference between human and computerized intelligence was devoid of visual influences. With today's VI bots, virtual intelligence has evolved past the constraints of past testing into a new level of the machine's ability to demonstrate intelligence. The immersive features of these environments provide nonverbal elements that affect the realism provided by virtually intelligent agents. Virtual intelligence is the intersection of these two technologies: Virtual environments: Immersive 3D spaces provide for collaboration, simulations, and role-playing interactions for training. Many of these virtual environments are currently being used for government and academic projects, including Second Life, VastPark, Olive, OpenSim, Outerra, Oracle's Open Wonderland, Duke University's Open Cobalt, and many others. Some of the commercial virtual worlds are also taking this technology into new directions, including the high-definition virtual world Blue Mars. Artificial intelligence (AI): AI is a branch of computer science that aims to create intelligent machines capable of performing tasks that typically require human intelligence. VI is a type of AI that operates within virtual environments to simulate human-like interactions and responses. == Applications == Cutlass Bomb Disposal Robot: Northrop Grumman developed a virtual training opportunity because of the prohibitive real-world cost and dangers associated with bomb disposal. By replicating a complicated system without having to learn advanced code, the virtual robot has no risk of damage, trainee safety hazards, or accessibility constraints. MyCyberTwin: NASA is among the companies that have used the MyCyberTwin AI technologies. They used it for the Phoenix rover in the virtual world Second Life. Their MyCyberTwin used a programmed profile to relay information about what the Phoenix rover was doing and its purpose. Second China: The University of Florida developed the "Second China" project as an immersive training experience for learning how to interact with the culture and language in a foreign country. Students are immersed in an environment that provides role-playing challenges coupled with language and cultural sensitivities magnified during country-level diplomatic missions or during times of potential conflict or regional destabilization. The virtual training provides participants with opportunities to access information, take part in guided learning scenarios, communicate, collaborate, and role-play. While China was the country for the prototype, this model can be modified for use with any culture to help better understand social and cultural interactions and see how other people think and what their actions imply. Duke School of Nursing Training Simulation: Extreme Reality developed virtual training to test critical thinking with a nurse performing trained procedures to identify critical data to make decisions and performing the correct steps for intervention. Bots are programmed to respond to the nurse's actions as the patient with their conditions improving if the nurse performs the correct actions.
Read more →
Rassd News Network

Rassd News Network, also known by its initials of RNN (Arabic:شبكة رصد الاخبارية), is an alternative media network based in Cairo, Egypt. RNN was launched as a Facebook-based news source launched on January 25, 2011. It quickly advanced to become a primary contributor of Egyptian revolution-related news that year. Applying the motto "From the people to the people," the citizen journalists who created RNN have since added a Twitter feed and launched an independent website dedicated to short news stories favored by an online audience. RNN is an organized citizen news network with four working committees; one for editing the news, another to support the correspondents covering Egypt, a third for managing the multimedia feeds and a fourth for staff functions such as development, training and public relations. RNN's Arabic name, Rassd, is an acronym that stands for Rakeb (observe), Sawwer (record) and Dawwen (blog). RNN created a Ustream channel on January 27, 2011, and a YouTube account a month later. The success of RNN and its new social media model is evidenced in its recent local network expansion into Libya, Morocco, Syria, Jerusalem and Turkey. Even so, one media scholar in the US (commenting in 2011) called the accuracy of RNN's reporting "fairly mediocre". RNN has endured closures of their Facebook profile and YouTube account as part of the attacks from private media, attempting to thwart their work and influence their content. == Use of RNN's news by international media == RNN has been a global source of Egyptian revolution-related news since its launch. During the early days of the citizen uprisings across the Middle East, major networks such as BBC, Reuters, Al Jazeera and Al Arabiya used some of Rassd's news and photos, and followed the network on Twitter. Three days after the online portal went live it was streaming video to MSNBC through its Facebook page. Then on February 5, 2011, Louisville's NBC-affiliate cited RNN, Cairo when it reported that President Hosni Mubarak had stepped down as head of Egypt's ruling party.
Read more →
Social business model

The social business model is use of social media tools and social networking behavioral standards by businesses for communication with customers, suppliers, and others. Combining social networking etiquette (being helpful, transparent and authentic) with business engagement on LinkedIn (for one-to-one interaction), Twitter (for immediacy) and Facebook (for content sharing) more fully involves employees in the organization and increases customer intimacy and trust. == Overview == Traditional business models, particularly in large organizations, have had as one common characteristic careful limitation of direct contact between those within the organization and those outside of it. Only certain specific individuals (most frequently in roles such as sales, customer service and field consulting) were designated as "customer-facing" personnel. Organizations further limited outside access to internal employees through filtering mechanisms such as publishing only a main switchboard number (whether routed through a live receptionist or an interactive voice response system) and generic "sales@" or "info@" email addresses. The Cluetrain Manifesto (written by Rick Levine, Christopher Locke, Doc Searls, and David Weinberger and published in 1999) was among the first books to predict the demise of this old order and the emergence of more open business models, though most of the business world was slow to adopt the book's recommended cultural changes. Thirteen years later, authors Dion Hinchcliffe and Peter Kim added structural underpinnings to the cultural shifts outlined in The Cluetrain Manifesto in their book, Social Business by Design. The book details many of the ways social media tools and practices are being adopted within organizations, to support both internal employee collaboration and external customer engagement (which the authors describe as the "bigger problem"). == Elements == In implementing the social business model, organizations apply social networking protocols and tools in a range of areas, potentially including: Marketing Customer Support Recruiting Crowdsourcing Internal employee collaboration Sales Product Development Supply Chain Operations Investor Relations == Characteristics of organizations adopting the social business model == Organizations that fully adopt the social business model will exhibit four key characteristics: Connected – employees will be able to seamlessly engage one-on-one in real-time with other employees and individuals outside the organization (customers, prospects, partners, media, etc.) using a variety of communications methods including text chat, voice, file sharing, email, and video chat. Social – employees will follow social networking etiquette (being authentic, helpful and transparent) in external interactions. The focus will be on answering questions and providing information rather than overt sales or promotion. Presence – these conversations may originate on the company's website or elsewhere online (e.g., publication websites, industry portals, or social networking sites such as LinkedIn or Facebook). Intelligent – organizations will use in-depth analytics to monitor connections, social interactions and presence; measure corresponding business results; and continually adjust and improve practices for increased effectiveness. == Technical and functional requirements == While much of the change inherent in adopting the social business model is cultural, it also requires process changes enabled by social business technology. Functional requirements for a social business technology platform include: Analytics (including the cost of engagement as well as various measures of return on investment such as leads, sales, referrals, recommendations, and retained customers). Integration with other social media and business tools such as CRM systems, partner relationship management (PRM) software, product development, website analytics, and employee-recruiting applications. Rules-based workflow (e.g. routing a comment to the appropriate individual for a response, based on content). Geolocation (so customers or prospects can be automatically routed to local sales or customer service representatives). Content sharing. Collaboration tools. Transparency (i.e., people should know who they are engaging with) Unified communications (the ability to engage via voice, text, video, email, and share a wide variety of file types) Storage (the ability to store interactions for legal, training, compliance or compensation purposes, and purge stored data when no longer needed based on company policy or regulatory requirements). Immediacy (real-time monitoring and response).
Read more →
Social media background check

A social media background check is an investigative technique that involves scrutinizing the social media profiles and activities of individuals, primarily for pre-employment screening and other official verifications. These checks are performed to review people's online behavioral history on social media websites such as Facebook, Twitter, and LinkedIn. Social media background checks have become a common part of recruitment processes, among other verification procedures. == History == In the early 21st century, with the rapid expansion of social media platforms such as Facebook, Twitter, and LinkedIn, employers began to use these channels to gather additional information about prospective employees. Initially, social media background checks were an informal aspect of recruitment, but they have gradually gained formal recognition as a crucial element in candidate screening. Proponents of social media background checks argue that such reviews provide insight into a candidate's professional interests and networks, though the reliability of such assessments remains contested among researchers. == Rise in society == The practice of social media background checks has seen a significant surge in the last decade. This rise can be attributed to the exponential increase in social media users and the growing awareness among organizations regarding the importance of hiring individuals who align with their values and culture. Various platforms provide services explicitly designed to conduct social media background checks efficiently, simplifying the process for businesses. Companies providing social media background check services, such as Ferretly and Certn, have received venture capital funding, reflecting investor interest in the sector. The incorporation of artificial intelligence into conducting AI-powered social media background checks also illustrates its continued popularity and that businesses are looking to ramp up and even automate their use. High-profile cases in which individuals faced employment or admission consequences for past social media posts have raised awareness of social media background checking practices. For example, director James Gunn faced termination from Marvel Studios in 2018 over past offensive tweets, though he was later rehired. Additionally, multiple college admissions officers have acknowledged reviewing applicants' social media profiles, though such practices vary by institution. == Evolution of ethical considerations == Social media background checks are not without controversy, raising significant ethical considerations that have evolved in recent years. Privacy advocates argue that social media background checks raise concerns about data use and discrimination, particularly given the use of personal information that may not reflect job-relevant behavior. Legal scholars debate whether reviewing publicly posted information constitutes a privacy violation under U.S. law. Researchers and critics note that social media profiles often present curated representations of users' lives and may not reflect workplace behavior or professional competence. Moreover, the accuracy of social media background checks has been called into question, with critics pointing out that these checks may not always yield reliable or comprehensive results. Critics also warn about potential misuse of information obtained from social media, including cyberbullying and harassment. A 2023 study by found that approximately 90% of employers incorporate social media into hiring processes, with over half of those surveyed reporting they had rejected candidates based on social media content. This informal approach operates largely outside federal compliance frameworks. Critics argue that without regulation, candidates lack dispute mechanisms available under regulatory frameworks like the Fair Credit Reporting Act (FCRA), which requires compliance when background checks formally influence employment decisions. In a hiring environment where the practice is already performed often on an individual basis, the introduction of systematic, regulated screening practices that meet federal compliance standards can present a better, fairer alternative for both employers and candidates. == Business considerations == From a business perspective, social media background checks can be a valuable tool in protecting an organization's reputation and maintaining a safe and respectful workplace environment. A well-conducted social media background check can identify potential red flags, helping to prevent instances of workplace harassment or other negative behaviors. However, businesses also face potential legal repercussions if social media background checks are conducted improperly, such as non-compliance with the Fair Credit Reporting Act (FCRA) in the United States. Critics argue that over-reliance on social media data may exclude qualified candidates whose professional competence is not reflected in their online presence. The proliferation of social media screening services has prompted legal and industry experts to emphasize the importance of compliance with the Fair Credit Reporting Act and relevant state privacy laws when conducting such checks.
Read more →
Shell Control Box

Shell Control Box (SCB) is a network security appliance that controls privileged access to remote IT systems, records activities in replayable audit trails, and prevents malicious actions. For example, it records as a system administrator updates a file server or a third-party network operator configures a router. The recorded audit trails can be replayed like a movie to review the events as they occurred. The content of the audit trails is indexed to make searching for events and automatic reporting possible. SCB is a Linux-based device developed by Balabit. It is an application level proxy gateway. In 2017, Balabit changed the name of the product to Privileged Session Management (PSM) and repositioned it as the core module of its Privileged Access Management solution. == Main Features == Balabit’s Privileged Session Management (PSM), Shell Control Box (SCB) is a device that controls, monitors, and audits remote administrative access to servers and network devices. It is a tool to oversee system administrators by controlling the encrypted connections used for administration. PSM (SCB) has full control over the SSH, RDP, Telnet, TN3270, TN5250, Citrix ICA, and VNC connections, providing a framework (with solid boundaries) for the work of the administrators. === Gateway Authentication === PSM (SCB) acts as an authentication gateway, enforcing strong authentication before users access IT assets. PSM can also integrate to user directories (for example, a Microsoft Active Directory) to resolve the group memberships of the users who access the protected servers. Credentials for accessing the server are retrieved transparently from PSM’s credential store or a third-party password management system by PSM impersonating the authenticated user. This automatic password retrieval protects the confidentiality of passwords as users can never access them. === Access Control === PSM controls and audits privileged access over the most wide-spread protocols such as SSH, RDP, or HTTP(s). The detailed access management helps to control who can access what and when on servers. It is also possible to control advanced features of the protocols, like the type of channels permitted. For example, unneeded channels like file transfer or file sharing can be disabled, reducing the security risk on the server. With PSM policies for privileged access can be enforced in one single system. === 4-eyes Authorization === To avoid accidental misconfiguration and other human errors, PSM supports the 4-eyes authorization principle. This is achieved by requiring an authorizer to allow administrators to access the server. The authorizer also has the possibility to monitor – and terminate - the session of the administrator in real-time, as if they were watching the same screen. === Real-time Monitoring and Session Termination === PSM can monitor the network traffic in real time, and execute various actions if a certain pattern (for example, a suspicious command, window title or text) appears on the screen. PSM can also detect specific patterns such as credit card numbers. In case of detecting a suspicious user action, PSM can send an e-mail alert or immediately terminate the connection. For example, PSM can block the connection before a destructive administrator command, such as the „rm” comes into effect. === Session Recording === PSM makes user activities traceable by recording them in tamper-proof and confidential audit trails. It records the selected sessions into encrypted, timestamped, and digitally signed audit trails. Audit trails can be browsed online, or followed real-time to monitor the activities of the users. PSM replays the recorded sessions just like a movie – actions of the users can be seen exactly as they appeared on their monitor. The Balabit Desktop Player enables fast forwarding during replays, searching for events (for example, typed commands or pressing Enter) and texts seen by the user. In the case of any problems (database manipulation, unexpected shutdown, etc.) the circumstances of the event are readily available in the trails, thus the cause of the incident can be identified. In addition to recording audit trails, transferred files can be also recorded and extracted for further analysis.
Read more →
Social Media (Age-Restricted Users) Bill

The Social Media (Age-Restricted Users) Bill is a member's bill by National Party Member of Parliament Catherine Wedd that seeks to ban children under the age of 16 years from accessing social media by forcing social media companies to implement age verification measures. It is modelled after the Australian government's Online Safety Amendment. In mid October 2025, the New Zealand Parliament confirmed plans to introduce the social media age restriction bill. == Background == In late November 2024, the Albanese government of Australia, with support from the opposition Coalition parties, passed the Online Safety Amendment creating a world-first age verification regime targeting social media platforms operating in the country. The ban targets several social media platforms including Facebook, Instagram, Kick, Reddit, Snapchat, Threads, TikTok, Twitch, X (formerly Twitter) and YouTube. These platforms were required to implement age verification systems and to remove under-age users by 10 December 2025, when the law change came into effect. == Draft provisions == The draft Social Media (Age-Restricted Users) Bill defines social media platforms as electronic platforms that enable social media interactions between two or more end-users, facilitates communication between multiple end-users and allows users to post content on the platform. The proposed bill requires social media companies to take action to prevent users under the age of 16 from creating accounts on their platforms. It also creates a framework for courts to impose fines on platforms that fail to take reasonable steps to prevent underaged users from accessing the platform. == Legislative history == === Draft legislation === On 6 May 2025, Wedd announced a private member's bill called the "Social Media (Age-Restricted Users) Bill" that would bar access to social media platforms for people under the age of 16 years. She said that she was motivated as the mother of four children to support families, parents and teachers' efforts to manage their children's online exposure and the passage of the Australian Online Safety Amendment legislation in December 2024. Since National's coalition partner ACT New Zealand had refused to support the bill, the Sixth National Government announce it as a member's bill rather than a government bill. Prime Minister Christopher Luxon has confirmed that National would seek cross-party support for the legislation. ACT MP and the Minister of Internal Affairs Brooke van Velden said that the Government would watch the implementation of the Australian social media age restriction policy. In October 2025, Wedd's bill was drawn from the parliamentary ballot. In addition, Labour Reuben Davidson drafted a similar member's bill that would hold social media providers responsible for restricting "harmful content" and imposed NZ$50,000 fines for non-compliance. In November 2025, Luxon reiterated his support for social media age restriction legislation and said the New Zealand government would introduce a bill in 2026 before the 2026 New Zealand general election. He also confirmed that Education Minister Erica Stanford was leading an investigation into what lessons could be learnt from the Australian legislation. At the request of ACT MP Parmjeet Parmar, Parliament's Education and Workforce Committee held an inquiry into a proposed social media ban in early October 2025. The committee was led by National MP Carl Bates and received 430 submissions from 400 groups and individuals. The committee also heard from 87 in-person submissions. On 10 December 2025, the committee made 12 recommendations including restricting social media access to persons under the age of 16, re-evaluating existing legislation such as the Films, Videos, and Publications Classification Act and the Harmful Digital Communications Act 2015, and regulating online platforms and Internet service providers. The ACT party released a dissenting view disagreeing with the need for a law restricting social media access to under-16 year olds. In mid-May 2026, the Government confirmed that work on the proposed bill to ban under-16 year olds from social media had been paused. The New Zealand Parliament held a debate on the proposed bill on 13 May following a select committee inquiry into the harms caused by social media platforms. While the opposition Labour Party has agreed to support the member's bill, the ACT and Green parties opposed the proposed bill on the grounds that the rules were easy to circumvent, that at-risk groups could become more isolated, and that social media also harmed other age groups. == Responses == === Academia and civil society === In late July 2025, the New Zealand Council for Civil Liberties (NZCCL) expressed concern that the proposed social media age restriction could infringe upon the New Zealand Bill of Rights Act 1990, the Privacy Act 2020 and the United Nations' Convention on the Rights of the Child. The NZCCL also questioned the practicality of age verification software, a social media age limit and whether it would fulfil its stated goal of combating online harm. In August 2025, University of Auckland criminologist and senior lecturer Claire Meehan expressed concern that the social media age restriction legislation would cut children from their friendship and support networks. She also said that children and young people were digital natives who could use VPNs to circumvent the ban. Similar sentiments were echoed by Victoria University of Wellington media and communications lecturer Alex Beattie and "Ocean Today" Instagram social media influencer "Charlie." In October 2025, New Zealand Initiative representative Dr Eric Crampton expressed concern that a social media age restriction would involve the introduction of digital IDs. He argued that a new law was unnecessary and said that parents could limit their children's exposure to social media via Google's Family Link and Apple's equivalent. Similarly, Institute of Economic Affairs public policy fellow Matthew Lesh and the British Free Speech Union expressed concerns that young people could use VPNs to circumvent a social media ban, citing the spike in VPN usage in the United Kingdom following the passage of the Online Safety Act 2023. The advocacy group B416's co-chair Anna Curzon advocated for a social media ban on underage users, stating that social media apps "are made to be addictive" and made it difficult for parents to relate with their children. In late November 2025, B416's co-founder Anna Mowbray expressed support for the Government's social media age restriction bill but expressed disappointment that Luxon had not timed his announcement with the launch of the group's campaign. Generation-Z Aotearoa co-founder Lola Fisher has called on the New Zealand Government to consult with young people on the development of the legislation. === Government agencies and departments === In early October 2025, Privacy Commissioner Michael Webster expressed concern that social media platforms requiring users to prove their age via digital IDs could raise privacy concerns. Webster suggested that age verification systems could relay on various documents including passports. He said that age estimation technologies had high error rates and that age inference technologies relied on data mining. === Political parties === In early May 2025, the National Party government expressed support for a social media age restriction legislation. By contrast, its coalition partner ACT has opposed such legislation. ACT leader David Seymour described the ban as hasty and unworkable since it did not involve parents. Meanwhile, New Zealand First leader Winston Peters expressed support for a social media age restriction but said the bill should be subject to a select committee inquiry. The opposition Labour Party leader Chris Hipkins has expressed interest in a social media age restriction legislation but emphasised the need for consensus. Meanwhile, Green Party co-leader Chlöe Swarbrick said she wanted to learn more about the bill but described it as simplistic. Fellow Greens co-leader Marama Davidson said that the proposed bill would punish children and young people for the harm caused by big tech platforms. === Tech companies === In early October 2025, representatives of TikTok and Meta Platforms cautioned against proposed social media ban on under-16 years olds. During a one-day parliamentary inquiry, Ella Woods-Joyce, TikTok's public policy lead for Australia and New Zealand, and Mia Garlick, Meta's regional director of policy, expressed concern that the social media age restriction could send children and young people to less regulated online spaces. Woods-Joyce highlighted TikTok's policy of closing down accounts belonging to users under the age of 13 years while Garlick highlighted Meta's policy of placing users under the age of 16 in private accounts by default. In early February 2026 Meta's vice president and global head of safety, Antigone Da
Read more →
Data set (IBM mainframe)

In the context of IBM mainframe computers in the IBM System/360 line and its successors, a data set (IBM preferred) or dataset is a computer file having a record organization. Use of this term began with, e.g., DOS/360 and OS/360, and is still used by their successors, including the current VSE and z/OS. Documentation for these systems historically preferred this term rather than file. A data set is typically stored on a direct access storage device (DASD) or magnetic tape, however unit record devices, such as punch card readers, card punches, line printers and page printers can provide input/output (I/O) for a data set (file). Data sets are not unstructured streams of bytes, but rather are organized in various logical record and block structures determined by the DSORG (data set organization), RECFM (record format), and other parameters. These parameters are specified at the time of the data set allocation (creation), for example with Job Control Language DD statements. Within a running program they are stored in the Data Control Block (DCB) or Access Control Block (ACB), which are data structures used to access data sets using access methods. Records in a data set may be fixed, variable, or “undefined” length. == Data set organization == For OS/360, the DCB's DSORG parameter specifies how the data set is organized. It may be CQ Queued Telecommunications Access Method (QTAM) in Message Control Program (MCP) CX Communications line group DA Basic Direct Access Method (BDAM) GS Graphics device for Graphics Access Method(GAM) IS Indexed Sequential Access Method (ISAM) MQ QTAM message queue in application PO Partitioned Organization PS Physical Sequential among others. Data sets on tape may only be DSORG=PS. The choice of organization depends on how the data is to be accessed, and in particular, how it is to be updated. Programmers utilize various access methods (such as QSAM or VSAM) in programs for reading and writing data sets. Access method depends on the given data set organization. == Record format (RECFM) == Regardless of organization, the physical structure of each record is essentially the same, and is uniform throughout the data set. This is specified in the DCB RECFM parameter. RECFM=F means that the records are of fixed length, specified via the LRECL parameter. RECFM=V specifies a variable-length record. V records when stored on media are prefixed by a Record Descriptor Word (RDW) containing the integer length of the record in bytes and flag bits. With RECFM=FB and RECFM=VB, multiple logical records are grouped together into a single physical block on tape or DASD. FB and VB are fixed-blocked, and variable-blocked, respectively. RECFM=U (undefined) is also variable length, but the length of the record is determined by the length of the block rather than by a control field. The BLKSIZE parameter specifies the maximum length of the block. RECFM=FBS could be also specified, meaning fixed-blocked standard, meaning all the blocks except the last one were required to be in full BLKSIZE length. RECFM=VBS, or variable-blocked spanned, means a logical record could be spanned across two or more blocks, with flags in the RDW indicating whether a record segment is continued into the next block and/or was continued from the previous one. This mechanism eliminates the need for using any "delimiter" byte value to separate records. Thus data can be of any type, including binary integers, floating-point, or characters, without introducing a false end-of-record condition. The data set is an abstraction of a collection of records, in contrast to files as unstructured streams of bytes. == Partitioned data set == A partitioned data set (PDS) is a data set containing multiple members, each of which holds a separate sub-data set, similar to a directory in other types of file systems. This type of data set is often used to hold load modules (old format bound executable programs), source program libraries (especially Assembler macro definitions), ISPF screen definitions, and Job Control Language. A PDS may be compared to a Zip file or COM Structured Storage. A Partitioned Data Set can only be allocated on a single volume and have a maximum size of 65,535 tracks. Besides members, a PDS contains also a directory. Each member can be accessed indirectly via the directory structure. Once a member is located, the data stored in that member are handled in the same manner as a PS (sequential) data set. Whenever a member is deleted, the space it occupied is unusable for storing other data. Likewise, if a member is re-written, it is stored in a new spot at the back of the PDS and leaves wasted “dead” space in the middle. The only way to recover “dead” space is to perform file compression. Compression, which is done using the IEBCOPY utility, moves all members to the front of the data space and leaves free usable space at the back. (Note that in modern parlance, this kind of operation might be called defragmentation or garbage collection; data compression nowadays refers to a different, more complicated concept.) PDS files can only reside on DASD, not on magnetic tape, in order to use the directory structure to access individual members. Partitioned data sets are most often used for storing multiple job control language files, utility control statements, and executable modules. An improvement of this scheme is a Partitioned Data Set Extended (PDSE or PDS/E, sometimes just libraries) introduced with DFSMSdfp for MVS/XA and MVS/ESA systems. A PDS/E library can store program objects or other types of members, but not both. BPAM cannot process a PDS/E containing program objects. PDS/E structure is similar to PDS and is used to store the same types of data. However, PDS/E files have a better directory structure which does not require pre-allocation of directory blocks when the PDS/E is defined (and therefore does not run out of directory blocks if not enough were specified). Also, PDS/E automatically stores members in such a way that compression operation is not needed to reclaim "dead" space. PDS/E files can only reside on DASD in order to use the directory structure to access individual members. == Generation Data Group == A Generation Data Group (GDG) is a group of non-VSAM data sets that are successive generations of historically-related data stored on an IBM mainframe (running OS/360 and its successors or DOS/360 and its successors). A GDG is usually cataloged. An individual member of the GDG collection is called a "Generation Data Set." The latter may be identified by an absolute number, ACCTG.OURGDG(1234), or a relative number: (-1) for the previous generation, (0) for the current one, and (+1) the next generation. A GDG specifies how many generations of a data set are to be kept and at what age a generation will be deleted. Whenever a new generation is created, the system checks whether one or more obsolete generations are to be deleted. The purpose of GDGs is to automate archival, using the command language JCL, the data set name given is generic. When DSN appears, the GDG data set appears along with the history number, where (0) is the most recent version (-1), (-2), ... are previous generations (+1) a new generation (see DD) Another use of GDGs is to be able to address all generations simultaneously within a JCL script without having to know the number of currently available generations. To do this, you have to omit the parentheses and the generation number in the JCL when specifying the dataset. === GDG JCL & features === Generation Data Groups are defined using either the BLDG statement of the IEHPROGM utility or the DEFINE GENERATIONGROUP statement of the newer IDCAMS utility, which allows setting various parameters. LIMIT(10) would limit the number of generations limit to 10. SCRATCH FOR (91) would retain each member, up to the limited#generations, at least 91 days. IDCAMS can also delete (and optionally uncatalog) a GDG. ==== Example ==== Creation of a standard GDG for five safety scopes, each at least 35 days old: Delete a standard GDG:
Read more →
Data custodian

In data governance groups, responsibilities for data management are increasingly divided between the business process owners and information technology (IT) departments. Two functional titles commonly used for these roles are data steward and data custodian. Data Stewards are commonly responsible for data content, context, and associated business rules. Data custodians are responsible for the safe custody, transport, storage of the data and implementation of business rules. Simply put, Data Stewards are responsible for what is stored in a data field, while data custodians are responsible for the technical environment and database structure. Common job titles for data custodians are database administrator (DBA), data modeler, ETL developer and data engineer. == Data custodian responsibilities == A data custodian ensures: Access to the data is authorized and controlled Data stewards are identified for each data set Technical processes sustain data integrity Processes exist for data quality issue resolution in partnership with data stewards Technical controls safeguard data Data added to data sets are consistent with the common data model Versions of master data are maintained along with the history of changes Change management practices are applied in maintenance of the database Data content and changes can be audited
Read more →
Discovery system (artificial intelligence)

A discovery system is an artificial intelligence system that attempts to discover new scientific concepts or laws. The aim of discovery systems is to automate scientific data analysis and the scientific discovery process. Ideally, an artificial intelligence system should be able to search systematically through the space of all possible hypotheses and yield the hypothesis - or set of equally likely hypotheses - that best describes the complex patterns in data. During the era known as the second AI summer (approximately 1978–1987), various systems akin to the era's dominant expert systems were developed to tackle the problem of extracting scientific hypotheses from data, with or without interacting with a human scientist. These systems included Autoclass, Automated Mathematician, Eurisko, which aimed at general-purpose hypothesis discovery, and more specific systems such as Dalton, which uncovers molecular properties from data. The dream of building systems that discover scientific hypotheses was pushed to the background with the second AI winter and the subsequent resurgence of subsymbolic methods such as neural networks. Subsymbolic methods emphasize prediction over explanation, and yield models which works well but are difficult or impossible to explain which has earned them the name black box AI. A black-box model cannot be considered a scientific hypothesis, and this development has even led some researchers to suggest that the traditional aim of science - to uncover hypotheses and theories about the structure of reality - is obsolete. Other researchers disagree and argue that subsymbolic methods are useful in many cases, just not for generating scientific theories. == Discovery systems from the 1970s and 1980s == Autoclass was a Bayesian Classification System written in 1986 Automated Mathematician was one of the earliest successful discovery systems. It was written in 1977 and worked by generating a modifying small Lisp programs Eurisko was a Sequel to Automated Mathematician written in 1984 Dalton is a still maintained program capable of calculating various molecular properties initially launched in 1983 and available in open source since 2017 Glauber is a scientific discovery method written in the context of computational philosophy of science launched in 1983 == Modern discovery systems (2009–present) == After a couple of decades with little interest in discovery systems, the interest in using AI to uncover natural laws and scientific explanations was renewed by the work of Michael Schmidt, then a PhD student in Computational Biology at Cornell University. Schmidt and his advisor, Hod Lipson, invented Eureqa, which they described as a symbolic regression approach to "distilling free-form natural laws from experimental data". This work effectively demonstrated that symbolic regression was a promising way forward for AI-driven scientific discovery. Since 2009, symbolic regression has matured further, and today, various commercial and open source systems are actively used in scientific research. Notable examples include Eureqa, now a part of DataRobot AI Cloud Platform, AI Feynman, and QLattice.
Read more →
WhoSay

WhoSay was an American social media service and branding platform for celebrities and their fans. Founded in Los Angeles in 2010, with financing by Creative Artists Agency (CAA), Amazon.com and other investors, it is notable for allowing its users to retain ownership rights over the content that they post to their accounts, through copyright branding, and for enabling users to post content to other social media sites like Twitter, Facebook, Instagram and Tumblr simultaneously. WhoSay describes itself as a "social celebrity magazine" whose editorial team keeps its users informed about the latest celebrity and entertainment news. Clients such as Dylan McDermott and Chris Rock lauded the service for its ability to add content to multiple social network sites easily. Rock in particular has commented on its ease of use for those who are not part of a tech-savvy demographic, commenting, "It's perfect for someone that's not 25." WhoSay's competitors included theAudience, which is operated by the William Morris Endeavor. == History == WhoSay was founded in March 2010, by Steve Ellis and the Los Angeles-based talent agency Creative Artists Agency (CAA). It was financed through investments Amazon.com (who along with CAA, holds a minority stake in the company), Comcast, Greylock Partners, and High Peak Ventures. The company's main headquarters are in The New York Times Building in Manhattan, with additional headquarters in CAA's office building in the Silicon Beach area of Los Angeles, and in London. The company was founded to protect celebrities' intellectual property and enable the celebrities themselves to profit themselves from their own content through copyright branding. Its chief executive is co-founder Steve Ellis, who, after leaving Getty Images, was contacted by CAA, who were looking to resolve the issue of celebrities losing the rights to their own photos and videos when uploading them to social network sites. Ellis explained WhoSay's mission thus: "We work with people who are constantly being utilized by third parties for the wrong reasons. [The company was formed] to give celebrities and other influential people a set of tools to allow them to manage and control their presence in the digital world." In this way, WhoSay is likened by Ellis to "a People magazine by the people themselves who are in it." The company started slowly, until CAA client Tom Hanks signed onto WhoSay three months after the service's launch. The company continued to maintain a low profile for the first three years of operation, during which it accumulated a client list of 1,500 actors, musicians and artists. Clients are accepted by the service on an invitation-only basis, although they are not restricted to Creative Artists clients. Among them are Kelly Clarkson, Julia Louis-Dreyfus, Paula Patton, Kevin Spacey, Jim Carrey, John Cusack, Bill Maher, Johnny Knoxville, Chelsea Handler, Eva Longoria, Spike Lee, Enrique Iglesias and Katie Couric. Clients are not charged for the service, and are given a share of any revenue that is generated by advertisements. They are also given the ability share in the database of e-mail addresses that come with registration, in order to communicate directly with fans. Actor Dylan McDermott was introduced to WhoSay by his agent, as a way of easily posting content to Facebook, Twitter, Tumblr and even China's Tencent social network with relative ease. McDermott comments, "When you put something out there, you can hit everything at one time. It makes it easy for me." Comedian Chris Rock has commented that WhoSay is ideal for people like him have developed difficulty in keeping track of different websites as they get older, saying, "It's perfect for someone that's not 25." In September 2013 WhoSay introduced a mobile application for consumers. By October 2013, the company's website attracted 12 million monthly visitors. In July 2014 Rob Gregory left his role as president of Newsweek's The Daily Beast to become WhoSay's chief revenue officer. Among his responsibilities are developing ways to monetize WhoSay's web and mobile products, such as premium advertising strategies and brand partnerships. WhoSay does not allow consumers to create accounts, nor does it include search features, making it difficult to access a celebrity's account unless a user is directed there from one of their other social pages. According to Ellis, consumers have enough social media choices, saying, "Frankly they don't really need the services that we provide, and there are a lot of very specific features built into our service that really only benefit someone who is of a high profile." By February 2015, WhoSay had amassed 4.8 million unique users, and expanded its accounts to companies that employ celebrities for branded content. Such companies include Lexus, which partnered with the company to promote a campaign in which actress Rosario Dawson, during the lead up to the 87th Academy Awards, released five short videos on her social media accounts. The videos feature her driving through Los Angeles in preparation for the grand opening of her pop-up store, which sells Studio One Eighty Nine, a clothing line tied to her foundation promoting African culture and content. That April, WhoSay partnered with Chevrolet's #BestDayEver social media campaign for April Fool's Day, enlisting Olivia Wilde, Norman Reedus, Alec Baldwin, Ian Somerhalder, and Nikki Reed to surprise students in four U.S. classrooms as their substitute teachers. For example, Baldwin, dressed as Abraham Lincoln, surprised students in an Occidental College class on U.S. Culture and Society. Other companies that WhoSay has partnered with include KFC, JCPenney, Dunkin' Donuts and Crest. In January 2018, the website was acquired by Viacom (now Paramount Global).
Read more →
Corporate surveillance

Corporate surveillance describes the practice of businesses monitoring and extracting information from their users, clients, or staff. This information may consist of online browsing history, email correspondence, phone calls, location data, and other private details. Acts of corporate surveillance frequently look to boost results, detect potential security problems, or adjust advertising strategies. These practices have been criticized for violating ethical standards and invading personal privacy. Critics and privacy activists have called for businesses to incorporate rules and transparency surrounding their monitoring methods to ensure they are not misusing their position of authority or breaching regulatory standards. Monitoring can feel intrusive and give the impression that the business does not promote ethical behavior among its personnel. Staff satisfaction, productivity, and staff turnover may all suffer as a result of the invasion of privacy. == Monitoring methods == Employers may be authorized to gather information through keystroke logging and mouse tracking, which involves recording the keys individuals interact with and cursor position on computers. In cases where employment contracts permit it, they may also monitor webcam activity on company-provided computers. Employers may be able to view the emails sent from business accounts and may be able to see the websites visited when using a corporate internet connection. The screenshot capability is another tool that enables companies to see what remote workers are doing. This feature, which can be found in tracking software, takes screenshots throughout the day at predetermined or arbitrary intervals. Additionally, people who don't work in offices are observed. For instance, it has been claimed that Amazon has incorporated tracking technology to monitor warehouse staff and delivery drivers. == Use of collected information == Information collected by corporations can be used for a variety of uses including marketing research, targeting advertising, fraud detection and prevention, ensuring policy adherence, preventing lawsuits, and safeguarding records and company assets. == Privacy concerns == Concerns over corporate privacy have become more important due to companies collection and manipulation of personal data. Since these practices have been recognized there has been a rising concern about both the security and the possible mishandling of the data accumulated. Social Media data collection and monitoring has been one of the most concerned areas regarding corporate surveillance. Recently, many employers on CareerBuilder have checked their potential candidates' social media activities before the hiring process. This approach can be excusable since it is important to be aware of a future employee or applicant's online presence, and how it might affect the company's reputation in the future. This is crucial since employers are often made legally responsible for their worker's digital actions. These data can also be used to enact political gains. The Facebook-Cambridge Analytica data scandal in 2018 revealed that its British branch to have surreptitiously sold American psychological data to the Trump campaign. This information was supposed to be private, but Facebook's inability to protect user information had reportedly not been a top priority of the company at the time. == Laws and regulations == The National Labor and Relations Act (NLRA) safeguards workplace democracy by giving workers in the private sector the basic freedom to demand better working conditions and choice of representation without fear of retaliation. General Data Protection Regulation (GDPR) outlines the broad responsibilities of data controllers and the "processors" that handle personal data on their behalf. They must adopt the necessary security measures in accordance with the risk involved in the data processing operations they carry out.[1] Electronics Communication Privacy Act (ECPA), as amended, provides protection for electronic, oral, and wire communications while they are being created, while they are being sent, and while they are being stored on computers. Email, phone calls, and electronically stored data are covered by the Act. == Sale of customer data == If it is business intelligence, data collected on individuals and groups can be sold to other corporations, so that they can use it for the aforementioned purpose. It can be used for direct marketing purposes, such as targeted advertisements on Google and Yahoo. These ads are tailored to the individual user of the search engine by analyzing their search history and emails (if they use free webmail services). For example, the world's most popular web search engine stores identifying information for each web search. Google stores an IP address and the search phrase used in a database for up to 2 years. Google also scans the content of emails of users of its Gmail webmail service, in order to create targeted advertising based on what people are talking about in their personal email correspondences. Google is, by far, the largest web advertising agency. Their revenue model is based on receiving payments from advertisers for each page-visit resulting from a visitor clicking on a Google AdWords ad, hosted either on a Google service or a third-party website. Millions of sites place Google's advertising banners and links on their websites, in order to share this profit from visitors who click on the ads. Each page containing Google advertisements adds, reads, and modifies cookies on each visitor's computer. These cookies track the user across all of these sites, and gather information about their web surfing habits, keeping track of which sites they visit, and what they do when they are on these sites. This information, along with the information from their email accounts, and search engine histories, is stored by Google to use for building a profile of the user to deliver better-targeted advertising. == Surveillance of workers == In 1993, David Steingard and Dale Fitzgibbons argued that modern management, far from empowering workers, had features of neo-Taylorism, where teamwork perpetuated surveillance and control. They argued that employees had become their own "thought police" and the team gaze was the equivalent of Bentham's panopticon guard tower. A critical evaluation of the Hawthorne Plant experiments has in turn given rise to the notion of a Hawthorne effect, where workers increase their productivity in response to their awareness of being observed or because they are gratified for being chosen to participate in a project. According to the American Management Association and the ePolicy Institute, who undertook a quantitative survey in 2007 about electronic monitoring and surveillance with approximately 300 US companies, "more than one fourth of employers have fired workers for misusing email and nearly one third have fired employees for misusing the Internet." Furthermore, about 30 percent of the companies had also fired employees for usage of "inappropriate or offensive language" and "viewing, downloading, or uploading inappropriate/offensive content." More than 40 percent of the companies monitor email traffic of their workers, and 66 percent of corporations monitor Internet connections. In addition, most companies use software to block websites such as sites with games, social networking, entertainment, shopping, and sports. The American Management Association and the ePolicy Institute also stress that companies track content that is being written about them, for example by monitoring blogs and social media, and scanning all files that are stored in a filesystem. == Government use of corporate surveillance data == The United States government often gains access to corporate databases, either by producing a warrant for it, or by asking. The Department of Homeland Security has openly stated that it uses data collected from consumer credit and direct marketing agencies—such as Google—for augmenting the profiles of individuals that it is monitoring. The US government has gathered information from grocery store discount card programs, which track customers' shopping patterns and store them in databases, in order to look for terrorists by analyzing shoppers' buying patterns. == Corporate surveillance of citizens == According to Dennis Broeders, "Big Brother is joined by big business". He argues that corporations are in any event interested in data on their potential customers and that placing some forms of surveillance in the hands of companies, results in companies owning video surveillance data for stores and public places. The commercial availability of surveillance systems has led to their rapid spread. Therefore it is almost impossible for citizens to maintain their anonymity. When businesses can monitor their customers, such customers run the risk of facing prejudice when applying for housing, loans, jobs, and other economic opportun
Read more →