Corona-Warn-App was the official and open-source COVID-19 contact tracing app used for digital contact tracing in Germany made by SAP and Deutsche Telekom subsidiary T-Systems. It had been downloaded 22.8 million times as of 19 November 2020 and 26.2 million times as of 18 March 2021. The app has been promoted by billboard and broadcast advertisements, e.g. in cooperation with the German Football Association (DFB) and other prominent companies. The German government has announced that the app would no longer exchange tracing information as of April 30, 2023 & would enter hibernation as of June 1, 2023. == Effectiveness == Experts believe that time saved by using the app can be critical for improving the effectiveness contact tracing efforts. Some virologists say when at least 60% of people in Germany use it, it would be very effective. == Functioning == The app works with the Exposure Notification Framework (what is implemented in Google Play Services for Android and in iOS) by using Bluetooth to exchange codes with app users that are within 1.5 meters of each other for a period of at least 10 minutes. Anyone who tests positive for COVID-19 can share this information voluntarily with the app. Other app users are then notified about when, how long and at what distance they had contact with the infected person within a 14-day period. Testing is available for persons on a voluntary basis. === Server architecture === Based on the Client–server model five servers are operated within the app backend: the Corona-Warn-App server. It stores the authorized keys of infected users, referred to as diagnosis keys, from the past 14 days in its database. Stored diagnosis keys are grouped into regularly updated blocks which are transmitted to the Content Delivery Network. This interface supplies the keys for the app clients to download and locally compute a potential exposure risk. the Verification server. It is responsible for documenting the approval of the user to share their positive test result with the app and also to verify the test result. the Portal Server. It generates a so-called teleTAN token if the user did not give their consent to share their test result with the app at first but then changed their mind or if the local public health authority or test laboratory is not connected to the app system yet. the Test Result Server. It saves the test results provided by the local public health authorities or test laboratories for further use within the backend. the Federation Gateway Server. It connects to the national Corona-Warn-App servers of participating EU countries to enable transnational key exchange. By the distribution of the data on different servers the decoupling of the data becomes possible and results in an obstructed tracing of the app users. ==== Report of a positive COVID-19 test ==== The app provides a function to warn other app users by uploading their positive test result on a voluntarily and anonymous basis to the Corona-Warn-App server. In case the local public health authority or test laboratory is already connected to the app system, the user receives a QR-Code when the swab specimen is taken that can be scanned in the app. After scanning the QR-Code und the user getting authorized by the Verification server, the app receives an individual Registration token which gets stored locally and with which the status and the result of the test can be checked manually as well as automatically. If the local public health authority or test laboratory is not connected to the app system yet and the user wants to share their positive test result with other app users, it is required to request a teleTAN token by calling the verification hotline of the app. In both cases, the user can upload their diagnosis keys of the last 14 days to the Corona-Warn-App server in case their consent to share the information is given. The Corona-Warn-App server then verifies the uploaded keys by asking the Verification server if the keys are valid and if they are, the Corona-Warn-App server stores them in its database. == Privacy == The use of the app is voluntary. The app implements decentralized data storage to ensure data privacy. Employers can require that Corona-Warn be installed on company phones, but can not compel its use on private phones. == Funding == The open source app, which costs €20 million to develop is intended to supplement human contact tracing efforts, which Germany put in place during the early stages of the COVID-19 pandemic in Germany. In August 2022, a spokesperson for the German ministry of health announced that the total costs including all additional developments are now estimated to be closer to €150m. == Interoperability == At its start the app only worked in Germany, and Jens Spahn, than Federal Minister of Health (CDU), has said the development of a Europe-wide system is a future goal. With the update published on 19 October 2020 the app supports key-exchanges with the EU Interoperability Gateway and is therefore able to communicate with contact tracing apps from Ireland and Italy. Austria, Belgium, Czech Republic, Croatia, Cyprus, Denmark, Finland, Ireland, Italy, Latvia, Malta, Netherlands, Norway, Poland, Slovenia, Spain and Switzerland had joined the gateway as well and are also able to exchange keys with Corona-Warn-App. The app can be downloaded in many App stores outside of Germany. However, as of August 2021, the app is still unavailable for those of notable national German minorities like Turks, Russians or Ukrainians, who use App stores of their home countries. == Software variants == An unofficial Corona-Warn-App has been released on F-Droid, making the app available without proprietary components on Android phones. == Literature == Thomas Köllmann: Die Corona-Warn-App – Schnittstelle zwischen Datenschutz- und Arbeitsrecht. In: Neue Zeitschrift für Arbeitsrecht. Nr. 13, 10. Juli 2020, S. 831–836.
Augmented Analytics
Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. Augmented analytics is based on business intelligence and analytics. In the graph extraction step, data from different sources are investigated. == Defining Augmented Analytics == Machine Learning – a systematic computing method that uses algorithms to sift through data to identify relationships, trends, and patterns. It is a process that allows algorithms to dynamically learn from data instead of having a set base of programmed rules. Natural language generation (NLG) – a software capability that takes unstructured data and translates it into plain-English, readable, language. Automating Insights – using machine learning algorithms to automate data analysis processes. Natural Language Query – enabling users to query data using business terms that are either typed onto a search box or spoken. == Data Democratization == Data Democratization is the democratizing data access in order to relieve data congestion and get rid of any sense of data "gatekeepers". This process must be implemented alongside a method for users to make sense of the data. This process is used in hopes of speeding up company decision making and uncovering opportunities hidden in data. There are three aspects to democratising data: Data Parameterisation and Characterisation. Data Decentralisation using an OS of blockchain and DLT technologies, as well as an independently governed secure data exchange to enable trust. Consent Market-driven Data Monetisation. When it comes to connecting assets, there are two features that will accelerate the adoption and usage of data democratisation: decentralized identity management and business data object monetization of data ownership. It enables multiple individuals and organizations to identify, authenticate, and authorize participants and organizations, enabling them to access services, data or systems across multiple networks, organizations, environments, and use cases. It empowers users and enables a personalized, self-service digital onboarding system so that users can self-authenticate without relying on a central administration function to process their information. Simultaneously, decentralized identity management ensures the user is authorized to perform actions subject to the system’s policies based on their attributes (role, department, organization, etc.) and/ or physical location. == Use cases == Agriculture – Farmers collect data on water use, soil temperature, moisture content and crop growth, augmented analytics can be used to make sense of this data and possibly identify insights that the user can then use to make business decisions. Smart Cities – Many cities across the United States, known as Smart Cities collect large amounts of data on a daily basis. Augmented analytics can be used to simplify this data in order to increase effectiveness in city management (transportation, natural disasters, etc.). Analytic Dashboards – Augmented analytics has the ability to take large data sets and create highly interactive and informative analytical dashboards that assist in many organizational decisions. Augmented Data Discovery – Using an augmented analytics process can assist organizations in automatically finding, visualizing and narrating potentially important data correlations and trends. Data Preparation – Augmented analytics platforms have the ability to take large amounts of data and organize and "clean" the data in order for it to be usable for future analyses. Business – Businesses collect large amounts of data, daily. Some examples of types of data collected in business operations include; sales data, consumer behavior data, distribution data. An augmented analytics platform provides access to analysis of this data, which could be used in making business decisions.
Wrike
Wrike, Inc. is an American project management application service provider based in San Jose, California. Wrike also has offices in India, Dallas, Tallinn, Nicosia, Dublin, Tokyo, Melbourne, and Prague. == History == Wrike was founded in 2006 by Andrew Filev. Currently CEO at Wrike is Thomas Scott. Filev initially self-funded the company before later obtaining investor funding. Wrike released the beta version of its software (also called Wrike) in December 2006. The company then launched a new "Enterprise" platform in December 2013. In June 2015, Wrike announced the opening of an office in Dublin, Ireland and in 2016, Wrike launched a datacenter there to host data in compliance with local privacy regulations. In July 2016, Wrike announced the launch of Wrike for Marketers. That same year, Wrike's headquarters moved from Mountain View to San Jose, California. In January 2021, Citrix Systems announced its intention to acquire Wrike for $2.25 billion. The acquisition closed in March 2021. On January 31, 2022, it was announced that Citrix had been acquired in a $16.5 billion deal by affiliates of Vista Equity Partners and Evergreen Coast Capital. Citrix would merge with TIBCO Software, a Vista portfolio company to form Cloud Software Group (CSG). In September 2022, Wrike separated from Citrix Systems. In July 2023, Vista transferred ownership to Symphony Technology Group. == Investments == Wrike received $1 million in Angel funding in 2012 from TMT Investments. In October, 2013, Wrike secured $10 million in investment funding from Bain Capital. In May 2015, the company secured $15 million in a new round of funding. Investors included Scale Venture Partners, DCM Ventures, and Bain Capital. At that time, Wrike had 8,000 customers, 200 employees, and 30,000 new users each month. On November 29, 2018, Wrike signed a definitive agreement to receive a majority investment by Vista Equity Partners (“Vista”), a firm focused on software, data and technology-enabled businesses. == Software == The Wrike project management software is a Software-as-a-Service (SaaS) product with tools for managing projects, deadlines, schedules, and workflow processes. It includes collaboration features. The application is available in English, French, Spanish, German, Portuguese, Italian, Japanese and Russian. Wrike has triggers for task automation in workflow management. === Features === Wrike features a multi-pane UI and consists of features in two categories: project management, and team collaboration. According to Wrike, project management features are designed to help teams track dates and dependencies associated with projects, manage assignments and resources, and track time. These include an interactive Gantt chart, a workload view, and a sortable table that can be customized to store project data. The software includes a co-editing tool, discussion threads on tasks, and tools for attaching documents, editing them, and tracking their changes. Wrike uses an "inbox" feature and browser notifications to alert users of updates from their colleagues and dashboards for quick overviews of pending tasks. These updates are also available in Wrike's mobile apps on iOS and Android. Wrike has an optional feature set called "Wrike for Marketers" which has several tools for managing marketing workflows. In May 2012, Wrike announced the launch of a freemium version of its software for teams of up to 5 users. That year also saw the integration of a live text coeditor into its workspace to unify collaboration and task management. In late 2013 Wrike released a new feature set called Wrike Enterprise which included advanced analytics and other tools targeted at large business customers. Since then it has released several major updates to Wrike Enterprise, including a customizable spreadsheet called "Dynamic Platform" in late 2014 and custom workflows for teams in 2015. In July 2016, Wrike was updated with a set of add-on features under the name "Wrike for Marketers," which includes integrations with Adobe Photoshop, a tool for submitting requests, and proofing and approval tools for creative assets like videos and images. Wrike is available as native Android and iOS apps. Mobile apps include an interactive Gantt chart that syncs across devices. The apps are available offline, and sync when connection is restored. === Criticism === Critics said new users may have a learning curve with complex features. Wrike has 2,710 customers for an estimated 0.04% market share. Competitors include Google Workspace, Slack (software), and Quip (software).
The Cancer Imaging Archive
The Cancer Imaging Archive (TCIA) is an open-access database of medical images for cancer research. The site is funded by the National Cancer Institute's (NCI) Cancer Imaging Program, and the contract is operated by the University of Arkansas for Medical Sciences. Data within the archive is organized into collections which typically share a common cancer type and/or anatomical site. The majority of the data consists of CT, MRI, and nuclear medicine (e.g. PET) images stored in DICOM format, but many other types of supporting data are also provided or linked to, in order to enhance research utility. All data are de-identified in order to comply with the Health Insurance Portability and Accountability Act and National Institutes of Health data sharing policies. TCIA resources are intended to support: Development of computer aided diagnosis methods (quantitative imaging) Evaluation of unbiased science reproducibility by acceptable standard statistical methods Research on correlation of clinical diagnostic medical images with digital microscopic histological images Exploratory biomarker research for which imaging is a key element Collaboration between cross-disciplinary investigators where imaging is crucial to research on tumor heterogeneity, between patients and within the tumor; tissue temporal response tracking - objective measurements of tumor progression; imaging genomics and Big Data linkages and analysis (clinical, histo-pathology, genomics) TCIA is recognized as a recommended repository for the Scientific Data, PLOS One, and F1000Research journals. It is also listed in the Registry of Research Data Repositories. == History == Prior to the creation of TCIA, the NCI funded development of the National Biomedical Imaging Archive. NBIA is an open-source Web application which was designed to allow the storage and query of DICOM images. TCIA was subsequently initiated in December 2010 to expand data sharing activities by funding a service component which would help address the technical and policy challenges associated with medical imaging research. TCIA leverages open-source tools such as NBIA and Clinical Trials Processor in order to provide its services. == Organization of the archive == The site content is organized into five categories: About Us - Provides a general overview of the site the organizations responsible for operating it. Share Your Data - Provides an overview of how to apply to upload data to the archive. Access the Archive - Provides information about the available data, methods for accessing that data and system usage metrics. Research Activities - Provides information about major research initiatives being conducted using TCIA data as well as information about publication guidelines. Help - Provides information about how to get support using the archive as well as documentation and data usage policies. == Methods for accessing data == Most collections on the Cancer Imaging Archive can be accessed without an account, but a few are restricted to specific users and therefore require an account to access them. TCIA has several ways to browse, filter, and download data. They include: Downloading the entire contents of a collection in bulk Leveraging the NBIA application to filter or search within or across collections Utilizing the RESTful Application programming interface to filter or search within or across collections === Browsing, bulk downloading and access to supporting data === The home page includes a list of all available collections. Basic information about the data such as the cancer type, cancer location, modalities, and number of subjects are also provided. Clicking on a collection name presents a page which describes the data including its original research purpose, how the data were generated, and how it might be useful to other TCIA users. For example, doi:10.7937/K9/TCIA.2015.L4FRET6Z describes the NSCLC-Radiomics-Genomics Collection. In the lower section of the page there are links to search or download the images and any available supporting data in the Data Access tab. Additional tabs provide information about data versions and how to cite the data if used in publications. Many collections contain additional data types such as genomics, patient demographics, treatment details, and expert analyses of the images. This data is usually only found by browsing the collection pages as opposed to searching in NBIA or using the API. === Filtering or searching with NBIA === On each Collection page and also in the main menu of the site there are links to "Search TCIA". This will load the NBIA application which allows simple, advanced and free text searches. Search results follow the conventional DICOM hierarchy of patient -> study -> series. TCIA provides comprehensive documentation on the various features of the NBIA software. === RESTful API === A number of search and download commands are also available through the API. New iterations on the API are released as new versions, so that existing applications developed against older versions of the API continue to function. == Research activities == A list of known publications based on TCIA data is maintained as a convenience to researchers who might want to investigate how it has been used previously. In addition to peer-reviewed publications there are also several major research initiatives described in the Research Activities section of the site. === The CIP TCGA Radiology Initiative for Radiogenomics Research === A large number of collections contain subjects which were analyzed as part of the NIH/NHGRI database known as The Cancer Genome Atlas (TCGA). This offers researchers the ability to correlate clinical images using shared unique identifiers each study that has in TCGA extensive genomic analysis, digital pathology slides and bulk download of individual demographic data and clinical data. A multi-institutional network of investigators volunteering their time is using the data to develop methods to determine prognosis or predict the response to therapy. TCGA collections are designated by nomenclature shared by the TCGA Data Portal (e.g.: TCGA-BRCA, TCGA-GBM, etc). They are subject to a special publication policy which is unique from the other public data on TCIA. === Challenge competitions === TCIA also provides specific data sets used for "Challenge" competitions such as international digital image-focused professional societies like MICCAI, SPIE, or ISBI. A directory of previous and upcoming challenges is maintained on the site. === Digital object identifiers === To facilitate data sharing, many publications encourage authors to include data citations to the data that the authors used in creating the results described in their scholarly papers. In addition, new journals are now available for describing data collections outright (e.g., Nature Scientific Data). TCIA assigns digital object identifiers (DOIs) to all collections when they are submitted, and also has the ability to create persistent identifiers linked to subsets of data held within TCIA that authors may use for data citations in their scholarly papers.
Flask (web framework)
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions that can add application features as if they were implemented in Flask itself. Extensions exist for object-relational mappers, form validation, upload handling, various open authentication technologies and several common framework related tools. Applications that use the Flask framework include Pinterest and LinkedIn. == History == Flask was created by Armin Ronacher of Pocoo, an international group of Python enthusiasts formed in 2004. According to Ronacher, the idea was originally an April Fool's joke that was popular enough to make into a serious application. The name is a play on the earlier Bottle framework. When Ronacher and Georg Brandl created a bulletin board system written in Python in 2004, the Pocoo projects Werkzeug and Jinja were developed. In April 2016, the Pocoo team was disbanded and development of Flask and related libraries passed to the newly formed Pallets project. Flask has become popular among Python enthusiasts. As of October 2020, it has the second-most number of stars on GitHub among Python web-development frameworks, only slightly behind Django, and was voted the most popular web framework in the Python Developers Survey for years between and including 2018 and 2022. == Components == The microframework Flask is part of the Pallets Projects (formerly Pocoo), and based on several others of them, all under a BSD license. === Werkzeug === Werkzeug (German for "tool") is a utility library for the Python programming language for Web Server Gateway Interface (WSGI) applications. Werkzeug can instantiate objects for request, response, and utility functions. It can be used as the basis for a custom software framework and supports Python 2.7 and 3.5 and later. === Jinja === Jinja, also by Ronacher, is a template engine for the Python programming language. Similar to the Django web framework, it handles templates in a sandbox. === MarkupSafe === MarkupSafe is a string handling library for the Python programming language. The eponymous MarkupSafe type extends the Python string type and marks its contents as "safe"; combining MarkupSafe with regular strings automatically escapes the unmarked strings, while avoiding double escaping of already marked strings. === ItsDangerous === ItsDangerous is a safe data serialization library for the Python programming language. It is used to store the session of a Flask application in a cookie without allowing users to tamper with the session contents. === Click === Click is a Python package used by Flask to create command-line interfaces (CLI) by providing a simple and composable way to define commands, arguments, and options. == Features == Development server and debugger Integrated support for unit testing RESTful request dispatching Uses Jinja templating Support for secure cookies (client side sessions) 100% WSGI 1.0 compliant Unicode-based Complete documentation Google App Engine compatibility Extensions available to extend functionality == Example == The following code shows a simple web application that displays "Hello World!" when visited: === Render Template with Flask === ==== Jinja in HTML for the Render Template ====
Action model learning
Action model learning (sometimes abbreviated action learning) is an area of machine learning concerned with the creation and modification of a software agent's knowledge about the effects and preconditions of the actions that can be executed within its environment. This knowledge is usually represented in a logic-based action description language and used as input for automated planners. Learning action models is important when goals change. When an agent acted for a while, it can use its accumulated knowledge about actions in the domain to make better decisions. Thus, learning action models differs from reinforcement learning. It enables reasoning about actions instead of expensive trials in the world. Action model learning is a form of inductive reasoning, where new knowledge is generated based on the agent's observations. The usual motivation for action model learning is the fact that manual specification of action models for planners is often a difficult, time-consuming, and error-prone task (especially in complex environments). == Action models == Given a training set E {\displaystyle E} consisting of examples e = ( s , a , s ′ ) {\displaystyle e=(s,a,s')} , where s , s ′ {\displaystyle s,s'} are observations of a world state from two consecutive time steps t , t ′ {\displaystyle t,t'} and a {\displaystyle a} is an action instance observed in time step t {\displaystyle t} , the goal of action model learning in general is to construct an action model ⟨ D , P ⟩ {\displaystyle \langle D,P\rangle } , where D {\displaystyle D} is a description of domain dynamics in action description formalism like STRIPS, ADL or PDDL and P {\displaystyle P} is a probability function defined over the elements of D {\displaystyle D} . However, many state of the art action learning methods assume determinism and do not induce P {\displaystyle P} . In addition to determinism, individual methods differ in how they deal with other attributes of domain (e.g. partial observability or sensoric noise). == Action learning methods == === State of the art === Recent action learning methods take various approaches and employ a wide variety of tools from different areas of artificial intelligence and computational logic. As an example of a method based on propositional logic, we can mention SLAF (Simultaneous Learning and Filtering) algorithm, which uses agent's observations to construct a long propositional formula over time and subsequently interprets it using a satisfiability (SAT) solver. Another technique, in which learning is converted into a satisfiability problem (weighted MAX-SAT in this case) and SAT solvers are used, is implemented in ARMS (Action-Relation Modeling System). Two mutually similar, fully declarative approaches to action learning were based on logic programming paradigm Answer Set Programming (ASP) and its extension, Reactive ASP. In another example, bottom-up inductive logic programming approach was employed. Several different solutions are not directly logic-based. For example, the action model learning using a perceptron algorithm or the multi level greedy search over the space of possible action models. In the older paper from 1992, the action model learning was studied as an extension of reinforcement learning. Nonetheless, further algorithms can be found that operate under different assumptions: FAMA can work even when some observations are missing, and it produces a general (lifted) planning model. It treats learning an action model like a planning problem, making sure the learned model matches the observations given. NOLAM can learn general action models even from noisy or imperfect data. LOCM focuses only on the order of actions in the data, ignoring any details about the states between those actions. The family of safe action model (SAM) learning methods create models that guarantee any plans made with them will actually work in the real world. There's also an extension called N-SAM that can learn action models with numeric conditions and effects. Additionally, numeric action models like N-SAM can be used to improve reinforcement learning (RL) performance through the RAMP algorithm. === Literature === Most action learning research papers are published in journals and conferences focused on artificial intelligence in general (e.g. Journal of Artificial Intelligence Research (JAIR), Artificial Intelligence, Applied Artificial Intelligence (AAI) or AAAI conferences). Despite mutual relevance of the topics, action model learning is usually not addressed in planning conferences like the International Conference on Automated Planning and Scheduling (ICAPS).
CEITON
CEITON is a web-based software system for facilitating and automating business processes such as planning, scheduling, and payroll using workflow technologies. The system is used by several media companies such as MDR, Yle, RAI and Red Bull Media House. In December 2018, the first CEITON User Group Meeting took place in Leipzig, Germany. == Architecture == The software runs on a server (on premises) or in the cloud and is scalable on parallel servers. Data security is warranted by role-based access control (RBAC). The software is used via web-browsers and not dependent on particular system software. == Structure and Features == CEITON combines the two classical approaches of production planning and control and workflow management. === Project Management === The scheduling system plans, manages, bills, and analyzes projects or tasks. It manages human and technical resources, material, and locations on a single GUI. The system uses a gantt chart to assign tasks to be done to available and eligible resources (i.e. staff), automatically or by drag-and-drop. The scheduling module includes material management, resource management/ human resource management, integration of freelancers, clients and suppliers, long-term budget planning, time-tracking, shift scheduling, quality management, delivery and logistics, document management, archive, analysis and controlling, business reporting, as well as all accounting and documentation processes. === Workflow === The workflow management system module coordinates business processes. Processes are defined once as a workflow and then repeatedly executed. Human resources are automatically assigned to steps (tasks) and integrated in workflow forms. Systems are integrated with an EAI/SOAP module, allowing data exchange with arbitrary external systems which are also involved in the business process. It also features a 3-D workflow overview in which the status of each project step can be determined by its color in the overview. === Process Management === For project and order processing management, business processes are designed as workflows, and coordinate communication automatically. Different user interfaces for staff, customers or suppliers can be created so each gets only relevant information. Different workflow forms are associated with different log-ins. The main application for the system is knowledge-based business processes, in which many people are involved and virtual results are produced, e.g. in research, or development of media products, such as TV and movies. Broadcasters and media companies such as MDR and Yle use CEITON to control their production processes for products and services and coordinate complex workflows with all kinds of resources. === Integrations === An integrated EAI module allows CEITON to integrate every external system in any business process without programming, using SOAP and similar technologies. Aspera and FileCatalyst were integrated for faster data transfer, yet complex ERP systems and numerous SAP modules have also been integrated, for example, to extract working times to payroll. === Mobile Working === Since Version 7, released in 2015, CEITON includes a time-tracking module allowing employees to enter their times from mobile devices such as tablets running Android, iPhones etc. == History == Ceiton Technologies (SME tech firm), the company developing CEITON, was founded in Leipzig, Germany in 2000, staffing solutions for the Bureau of Internal Revenue in Manila, Philippines, were implemented in 2000 together with the Deutsche Gesellschaft für Technische Zusammenarbeit of the German government. The first version (1.0) of the software was released in July 2001. The product was originally developed for German broadcasting companies. CEITON is named after the Japanese concept Seiton, one of the principles of Japanese workplace design methodology known as 5S. Since version 7, released in 2015, CEITON includes a time-tracking module allowing employees to enter their times from mobile devices such as tablets running Android, iPhones etc. In May 2005 CEITON won the IQ innovation award, sponsored by Siemens, in the category Excellent innovation in the IT-sector. Since 2007, CEITON has been present at the broadcast trade fairs NAB in Las Vegas and IBC in Amsterdam. In 2020, the company celebrated its 20th anniversary.