Media evaluation is a discipline of the external and logical social sciences and centres on the analysis of media content, rating the exposure using a number of pre-designated criteria commonly including tonal value and presence of key messages. It is said to be one of the fastest-growing areas of mass communications research. The International Association for Measurement and Evaluation of Communication (AMEC) is the industry-appointed trade body for companies and individuals involved in research, measurement, and evaluation in editorial media coverage and related communications issues. To be a full member of AMEC, companies must be able to: a) offer comprehensive media evaluation, research, and interpretation services, b) have been in business for at least two years, and c) have a media evaluation turnover of more than £150,000 when applying. In addition, all companies abide by a strict code of ethics and must implement tight quality control procedures. These requirements guarantee that all media evaluation services provided are of the highest caliber. The Commission on Public Relations Measurement & Evaluation is a different organization that was established in 1998 under the direction of the Institute for Public Relations. The Commission's main functions are to set standards and procedures for research and measurement in public relations and to publish authoritative white papers on best practices.
Toad (software)
Toad is a database management toolset from Quest Software for managing relational and non-relational databases using SQL aimed at database developers, database administrators, and data analysts. The Toad toolset runs against Oracle, SQL Server, IBM DB2 (LUW & z/OS), SAP and MySQL. A Toad product for data preparation supports many data platforms. == History == A practicing Oracle DBA, Jim McDaniel, designed Toad for his own use in the mid-1990s. He called it Tool for Oracle Application Developers, shortened to "TOAD". McDaniel initially distributed the tool as shareware and later online as freeware. Quest Software acquired TOAD in October 1998. Quest Software itself was acquired by Dell in 2012 to form Dell Software. In June 2016, Dell announced the sale of their software division, including the Quest business, to Francisco Partners and Elliott Management Corporation. On October 31, 2016, the sale was finalized. On November 1, 2016, the sale of Dell Software to Francisco Partners and Elliott Management was completed, and the company re-launched as Quest Software. == Features == Connection Manager - Allow users to connect natively to the vendor’s database whether on-premise or DBaaS. Browser - Allow users to browse all the different database/schema objects and their properties effective management. Editor - A way to create and maintain scripts and database code with debugging and integration with source control. Unit Testing (Oracle) - Ensures code is functionally tested before it is released into production. Static code review (Oracle) - Ensures code meets required quality level using a rules-based system. SQL Optimization - Provides developers with a way to tune and optimize SQL statements and database code without relying on a DBA. Advanced optimization enables DBAs to tune SQL effectively in production. Scalability testing and database workload replay - Ensures that database code and SQL will scale properly before it gets released into production. == Books == Toad Pocket Reference for Oracle plsql 1st Edition by Jim McDaniel and Patrick McGrath, O'Reilly, 2002 (ISBN 0596003374, ISBN 978-0-596-00337-1) Toad Pocket Reference for Oracle 2nd Edition by Jeff Smith, Bert Scalzo, and Patrick McGrath, O'Reilly, 2005 (ISBN 0596009712, ISBN 978-0-596-00971-7) TOAD Handbook by Bert Scalzo and Dan Hotka, Sams, 2003 (ISBN 0672324865, ISBN 978-0-672-32486-4) TOAD Handbook 2nd Edition by Bert Scalzo and Dan Hotka, Addison-Wesley Professional, 2009 (ISBN 0321649109, ISBN 978-0-321-64910-2). TOAD Handbook 2nd Edition by Bert Scalzo and Dan Hotka, Addison-Wesley Professional, 2009 (ISBN 0321649109, ISBN 978-0-321-64910-2).
Anomaly detection
In data analysis, anomaly detection (also referred to as outlier detection and sometimes as novelty detection) is generally understood to be the identification of rare items, events or observations which deviate significantly from the majority of the data and do not conform to a well defined notion of normal behavior. Such examples may arouse suspicions of being generated by a different mechanism, or appear inconsistent with the remainder of that set of data. Anomaly detection finds application in many domains including cybersecurity, medicine, machine vision, statistics, neuroscience, law enforcement and financial fraud to name only a few. Anomalies were initially searched for clear rejection or omission from the data to aid statistical analysis, for example to compute the mean or standard deviation. They were also removed to better predictions from models such as linear regression, and more recently their removal aids the performance of machine learning algorithms. However, in many applications anomalies themselves are of interest and are the observations most desirous in the entire data set, which need to be identified and separated from noise or irrelevant outliers. Three broad categories of anomaly detection techniques exist. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier. However, this approach is rarely used in anomaly detection due to the general unavailability of labelled data and the inherent unbalanced nature of the classes. Semi-supervised anomaly detection techniques assume that some portion of the data is labelled. This may be any combination of the normal or anomalous data, but more often than not, the techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model. Unsupervised anomaly detection techniques assume the data is unlabelled and are by far the most commonly used due to their wider and relevant application. == Definition == Many attempts have been made in the statistical and computer science communities to define an anomaly. The most prevalent ones include the following, and can be categorised into three groups: those that are ambiguous, those that are specific to a method with pre-defined thresholds usually chosen empirically, and those that are formally defined: === Ill defined === An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Anomalies are instances or collections of data that occur very rarely in the data set and whose features differ significantly from most of the data. An outlier is an observation (or subset of observations) which appears to be inconsistent with the remainder of that set of data. An anomaly is a point or collection of points that is relatively distant from other points in multi-dimensional space of features. Anomalies are patterns in data that do not conform to a well-defined notion of normal behaviour. === Specific === Let T be observations from a univariate Gaussian distribution and O a point from T. Then the z-score for O is greater than a pre-selected threshold if and only if O is an outlier. == History == === Intrusion detection === The concept of intrusion detection, a critical component of anomaly detection, has evolved significantly over time. Initially, it was a manual process where system administrators would monitor for unusual activities, such as a vacationing user's account being accessed or unexpected printer activity. This approach was not scalable and was soon superseded by the analysis of audit logs and system logs for signs of malicious behavior. By the late 1970s and early 1980s, the analysis of these logs was primarily used retrospectively to investigate incidents, as the volume of data made it impractical for real-time monitoring. The affordability of digital storage eventually led to audit logs being analyzed online, with specialized programs being developed to sift through the data. These programs, however, were typically run during off-peak hours due to their computational intensity. The 1990s brought the advent of real-time intrusion detection systems capable of analyzing audit data as it was generated, allowing for immediate detection of and response to attacks. This marked a significant shift towards proactive intrusion detection. As the field has continued to develop, the focus has shifted to creating solutions that can be efficiently implemented across large and complex network environments, adapting to the ever-growing variety of security threats and the dynamic nature of modern computing infrastructures. == Applications == Anomaly detection is applicable in a very large number and variety of domains, and is an important subarea of unsupervised machine learning. As such it has applications in cyber-security, intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, detecting ecosystem disturbances, defect detection in images using machine vision, medical diagnosis and law enforcement. === Intrusion detection === Anomaly detection was proposed for intrusion detection systems (IDS) by Dorothy Denning in 1986. Anomaly detection for IDS is normally accomplished with thresholds and statistics, but can also be done with soft computing, and inductive learning. Types of features proposed by 1999 included profiles of users, workstations, networks, remote hosts, groups of users, and programs based on frequencies, means, variances, covariances, and standard deviations. The counterpart of anomaly detection in intrusion detection is misuse detection. === Fintech fraud detection === Anomaly detection is vital in fintech for fraud prevention. === Preprocessing === Preprocessing data to remove anomalies can be an important step in data analysis, and is done for a number of reasons. Statistics such as the mean and standard deviation are more accurate after the removal of anomalies, and the visualisation of data can also be improved. In supervised learning, removing the anomalous data from the dataset often results in a statistically significant increase in accuracy. === Video surveillance === Anomaly detection has become increasingly vital in video surveillance to enhance security and safety. With the advent of deep learning technologies, methods using Convolutional Neural Networks (CNNs) and Simple Recurrent Units (SRUs) have shown significant promise in identifying unusual activities or behaviors in video data. These models can process and analyze extensive video feeds in real-time, recognizing patterns that deviate from the norm, which may indicate potential security threats or safety violations. An important aspect for video surveillance is the development of scalable real-time frameworks. Such pipelines are required for processing multiple video streams with low computational resources. === IT infrastructure === In IT infrastructure management, anomaly detection is crucial for ensuring the smooth operation and reliability of services. These are complex systems, composed of many interactive elements and large data quantities, requiring methods to process and reduce this data into a human and machine interpretable format. Techniques like the IT Infrastructure Library (ITIL) and monitoring frameworks are employed to track and manage system performance and user experience. Detected anomalies can help identify and pre-empt potential performance degradations or system failures, thus maintaining productivity and business process effectiveness. === IoT systems === Anomaly detection is critical for the security and efficiency of Internet of Things (IoT) systems. It helps in identifying system failures and security breaches in complex networks of IoT devices. The methods must manage real-time data, diverse device types, and scale effectively. Garg et al. have introduced a multi-stage anomaly detection framework that improves upon traditional methods by incorporating spatial clustering, density-based clustering, and locality-sensitive hashing. This tailored approach is designed to better handle the vast and varied nature of IoT data, thereby enhancing security and operational reliability in smart infrastructure and industrial IoT systems. === Petroleum industry === Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. A 2015 paper proposed a novel segmentation algorithm using support vector machines to analyze sensor data for real-time anomaly detection. === Oil and gas pipeline monitoring === In the oil and gas sector, anomaly detection is not just crucial for maintenance and safety, but also for environmental protection. Aljameel et al. propose an advanced machine learning-based model for detecting minor leaks in oil and gas pipelines, a task traditional methods may miss.
Hybrid intelligent system
Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: Neuro-symbolic systems Neuro-fuzzy systems Hybrid connectionist-symbolic models Fuzzy expert systems Connectionist expert systems Evolutionary neural networks Genetic fuzzy systems Rough fuzzy hybridization Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods. From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years, there has been an increasing discussion of the importance of A.I. Systems Integration. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) and now is the time for integration to create broad AI systems. Proponents of this approach are researchers such as Marvin Minsky, Ron Sun, Aaron Sloman, Angelo Dalli and Michael A. Arbib. An example hybrid is a hierarchical control system in which the lowest, reactive layers are sub-symbolic. The higher layers, having relaxed time constraints, are capable of reasoning from an abstract world model and performing planning (even by hybrid wisdom). Intelligent systems usually rely on hybrid reasoning processes, which include induction, deduction, abduction and reasoning by analogy.
Dataset shift
Dataset shift is a phenomenon in machine learning and statistics in which the joint distribution of input variables and target labels is different in the training phase and the deployment or test phase (i.e., P t r a i n ( X , Y ) ≠ P t e s t ( X , Y ) {\displaystyle P_{train}(X,Y)\neq P_{test}(X,Y)} ). This happens when the statistical properties of data used to train a model are no longer representative of the data encountered in real-world use, often resulting in degraded predictive performance and diminished generalization ability. Dataset shift is a generic term for a number of particular types of distributional change. Covariate shift is when the distribution of the input features changes, but the conditional relationship between inputs and outputs remains constant . Prior probability shift (or label shift) happens when the distribution of target labels changes, but the conditional distribution of inputs given labels stays the same. Concept shift (also known as concept drift) is the change of the conditional relationship between inputs and outputs that renders previously learned patterns invalid over time. A key challenge for deploying machine learning systems is dataset shift, in particular in dynamic environments where the data distributions change over time. Detecting and mitigating such shifts is an active area of research, e.g., drift detection, domain adaptation, continual learning.
Geofence warrant
A geofence warrant or a reverse location warrant is a search warrant issued by a court to allow law enforcement to search a database to find all active mobile devices within a particular geo-fence area. Courts have granted law enforcement geo-fence warrants to obtain information from databases such as Google's Sensorvault, which collects users' historical geolocation data. Geo-fence warrants are a part of a category of warrants known as reverse search warrants. == History == Geofence warrants were first used in 2016. Google reported that it had received 982 such warrants in 2018, 8,396 in 2019, and 11,554 in 2020. A 2021 transparency report showed that 25% of data requests from law enforcement to Google were geo-fence data requests. Google is the most common recipient of geo-fence warrants and the main provider of such data, although companies including Apple, Snapchat, Lyft, and Uber have also received such warrants. == Legality == === United States === Some lawyers and privacy experts believe reverse search warrants are unconstitutional under the Fourth Amendment to the United States Constitution, which protects people from unreasonable searches and seizures, and requires any search warrants be specific to what and to whom they apply. The Fourth Amendment specifies that warrants may only be issued "upon probable cause, supported by Oath or affirmation, and particularly describing the place to be searched, and the persons or things to be seized." Some lawyers, legal scholars, and privacy experts have likened reverse search warrants to general warrants, which were made illegal by the Fourth Amendment. Groups including the Electronic Frontier Foundation have opposed geo-fence warrants in amicus briefs filed in motions to quash such orders to disclose geo-fence data. In 2024, a panel of the United States Fourth Circuit Court of Appeals considered data acquired from Google’s Sensorvault not to be a search, but non-private business records when users opt-in to Google’s location history. However, upon a rehearing en banc, the Court vacated that decision. In April 2025, the full Court affirmed the judgment solely on the 'good faith' exception, leaving the underlying constitutional question of whether geofence warrants constitute a search unsettled in the Circuit. However, the United States Fifth Circuit Court of Appeals found that geofence warrants are "categorically prohibited by the Fourth Amendment." The split in Circuits prompted the United States Supreme Court to agree to hear Chatrie v. United States in January 2026.
Spatial embedding
Spatial embedding is one of feature learning techniques used in spatial analysis where points, lines, polygons or other spatial data types. representing geographic locations are mapped to vectors of real numbers. Conceptually it involves a mathematical embedding from a space with many dimensions per geographic object to a continuous vector space with a much lower dimension. Such embedding methods allow complex spatial data to be used in neural networks and have been shown to improve performance in spatial analysis tasks == Embedded data types == Geographic data can take many forms: text, images, graphs, trajectories, polygons. Depending on the task, there may be a need to combine multimodal data from different sources. The next section describes examples of different types of data and their uses. === Text === Geolocated posts on social media can be used to acquire a library of documents bound to a given place that can be later transformed to embedded vectors using word embedding techniques. === Image === Satellites and aircraft collect digital spatial data acquired from remotely sensed images which can be used in machine learning. They are sometimes hard to analyse using basic image analysis methods and convolutional neural networks can be used to acquire an embedding of images bound to a given geographical object or a region. === Point === A single point of interest (POI) can be assigned multiple features that can be used in machine learning. These could be demographic, transportation, meteorological, or economic data, for example. When embedding single points, it is common to consider the entire set of available points as nodes in a graph. === Line / multiline === Among other things, motion trajectories are represented as lines (multilines). Individual trajectories are embedded taking into account travel time, distances and also features of points visited along the way. Embedding of trajectories allows to improve performance of such tasks as clustering and also categorization. === Polygon === The geographic areas analyzed in machine learning are defined by both administrative boundaries and top-down division into grids of regular shapes such as rectangles, for example. Both types are represented as polygons and, like points, can be assigned different demographic, transportation, or economic features. A polygon can also have features related to the size of the area or shape it represents. === Graph === An example domain where graph representation is used is the street layout in a city, where vertices can be intersections and edges can be roads. The vertices can also be destination points like public transport stops or important points in the city, and the edges represent the flow between them. Embedding graphs or single vertices allows to improve accuracy of analysis methods in which the treated geographical domain can be represented as a network. == Usage == POI recommendation - generating personalized point of interest recommendations based on user preferences. Next/future location prediction - prediction of the next location a person will go to based on their historical trajectory. Zone functions classification - based on different mobility of people or POI distribution a function of a given area in a city can be predicted. Crime prediction - estimation of crime rate in different regions of a city. Local event detection - studying spatio-temporal changes in embeddings can provide valuable information in detection of local event occurring in specific location. Regional mobility popularity prediction - analysis of mobility can show patterns in popularity of different regions in a city. Shape matching - finding a similar shape of given polygon, for example finding building with the same shape as input building. Travel time estimation - predicting estimated travel time given current traffic conditions and special occurring events. Time estimation for on-demand food delivery - estimation of delivery time when placing an order through the website. == Temporal aspect == Some of the data analyzed has a timestamp associated with it. In some cases of data analysis this information is omitted and in others it is used to divide the set into groups. The most common division is the separation of weekdays from weekends or division into hours of the day. This is particularly important in the analysis of mobility data, because the characteristics of mobility during the week and at different times of the day are very different from each other. Another area in which time division into, for example, individual months can be used is in the analysis of tourism of a given region. In order to take such a split into account, embedding methods treat the time stamp specifically or separate versions of the model are developed for different subgroups of the analyzed set.