Observing Key Topics From Multi-Channel Audio Streams
There is a large quantity of information communicated through speech audio streams (phone calls, broadcasts, commentary etc.) across many industries. In many instances this information is of potentially high value but is discarded for lack of efficient and accurate information retrieval strategies. Speech recognition technologies partially solve this problem but the output texts are still high volume, noisy and difficult to interpret and summarize.
Identifying Persons of Interest in Social Networks
Finding persons of interest on social media is not a trivial task and current approaches to people search are simplistic and based on collaborative filtering. There is a growing need to find people in a particular area of expertise, location, and/or business. IdentityMatch provides near real time retrieval of results from user searches.
Personalised forecasting model for more accurate predictions
Different areas of the economy require precise forecasts of future events based on knowledge collected from the past.Technology platforms which are accurate and effective in acquiring and processing such information are of great value to the market because they assist in planning and helping to prepare for what is coming. Unlike typical forecasting which can be based on informal methods, optimal forecasting methodology can be conducted based on scientific tools to assure proper outputs. As a result, appropriate feedback on a given problem can successfully warn if the process under investigation is heading towards an undesired direction. This result, in turn, can support managers with meaningful information that allows them to improve planning. An automated forecasting platform can result in trustworthy analytics which leads to better decision making processes. Successful forecasting can minimize risk which is associated with the unknown future, and if properly utilized may give rise to the profits which would not be achievable otherwise.
High-throughput, Scalable Data Stream Clustering
High volume, high throughput data streams are common in many industries including financial services (transaction streams), communications (instant messaging, SMS, micro-blogging), web and gaming (action and event streams) and production line environments (machine generated data). The ability to analyse and gain insights from this type of data as events happen, in real-time, can be hugely beneficial. Traditionally data analytics is performed as an off-line, batch process where the results are available hours or even days after the data was produced. This means that any actions taken based on these insights will be at a considerable time interval after the original events occurred, and in many scenarios being able to analyse the live data stream and hence reduce this response delay is of critical importance. Clustering is a core data analytics technique whereby similar entities are automatically identified and grouped together. This drives many common applications of data analytics such as detecting anomalous or fraudulent activity, identifyting market segments and user behaviours, reporting spam and emerging topics and patterns. CeADAR has developed a high-throughput, scalable clustering solution for data streams that brings real-time, ‘live data’ capabilities to these advanced data analytics tasks.
Advanced monitoring of entities in online media sources
In the always-connected 24-7 online marketplace, up-to-date knowledge is paramount for good decision making. In many timezones across the globe, content is emerging which can have an effect on the reputation of a firm, individual or product. Depending on the trust level attached to the source, a single social media message can cause significant damage to the reputation of an entity, as experienced when a tweet was sent from the hacked Associated Press Twitter account, causing the Dow Jones Index on Wall Street to drop sharply. Online monitoring of the reputation of an entity enables quick reactions to current events or market fluctuations.
PSAT is a Matlab-based suite for power system steady-state and dynamic analysis
PSAT is a Matlab toolbox for electric power system analysis and control. The command line version of PSAT is also GNU Octave compatible. PSAT includes power flow, continuation power flow, optimal power flow, small signal stability analysis and time domain simulation. All operations can be assessed by means of graphical user interfaces (GUIs) and a Simulink-based library provides an user-friendly tool for network design. ** This purchase price is for the instant delivery of electronic documentation, software is provided as open source **
Dome is a Python-based suite for power system modelling and simulation
Dome is a C and Python-based set of libraries for electric power system analysis and control. It is a Unix command line application that links to the state-of-the-art of mathematical libraries for sparse matrix factorization, synthesis of controllers and stability analysis. Parallel computing and GPU support is also provided. A reduced version of the tool can also run on Windows.
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