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themes [2016/10/11 11:15] philippereneviergonin created |
themes [2016/10/11 11:23] philippereneviergonin [Knowledge Extraction and Learning] |
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- | the SPARKS team is composed by 3 Themes. | + | The SPARKS team is composed by 3 Themes. |
===== Knowledge Extraction and Learning ===== | ===== Knowledge Extraction and Learning ===== | ||
+ | The activity in this research theme has mainly focused on the development of methods and algorithms | ||
+ | exploiting ideas and techniques from machine learning, data mining, natural language processing, and artificial | ||
+ | intelligence for extracting novel, useful information and knowledge from data. Many kinds of data have been | ||
+ | considered, ranging from structured data in tabular form (records with numerical and/or categorical attributes) | ||
+ | to unstructured data such as text in natural language, graphs (as diverse as social networks, see | ||
+ | also FORUM theme, gene regulatory networks, and linked data) and multimedia data, including time | ||
+ | series (e.g., electrocardiographic signals), images, videos and 3D data. | ||
+ | The methods and techniques used include support-vector machines, neural networks, | ||
+ | boosting, decision trees, random forests, frequent pattern and association rule mining , inductive logic programming, fuzzy set theory, and evolutionary algorithms. | ||
+ | A particular emphasis has been given to the scalability of the approaches, according to three dimensions: | ||
+ | - the volume of the data that are to be processed; | ||
+ | - the number of processing units available for computation and the distributed nature of both data and algorithms; | ||
+ | - the computational power of the processing units, which may become critical when the proposed approaches must be embedded in appliances, vehicles, or mobile devices, often with tight resource consumption constraints. | ||
- | ===== FOrmalizing and Reasoning with Users and Models ===== | + | ===== FOrmalizing and Reasoning with Users and Models (FORUM) ===== |
+ | Activities within this research theme address the general problem of reconciling formal semantics of computer | ||
+ | science (e.g. logics, ontologies, typing systems, protocols, etc.) on which the Web architecture is built, with | ||
+ | soft semantics of people (e.g. posts, tags, status, relationships, etc.) on which the Web content is built. The | ||
+ | research works in this theme contribute to the understanding of these graphs by (1) proposing multidisciplinary | ||
+ | approaches to analyze and model the many aspects of these intertwined information systems, their communities | ||
+ | of users and their interactions, and (2) formalizing and reasoning on these models using graph-based knowledge | ||
+ | representation from the semantic Web to propose new analysis tools and indicators, and support new | ||
+ | functionalities and better management. | ||
+ | The research objectives of this theme can be grouped according to three main topics: (1) supporting Human Computer | ||
+ | Interaction (HCI) and Human Data Interaction (HDI), (2) supporting interaction between users | ||
+ | and (3) reasoning and interacting with knowledge graphs. As a corollary, we design user-centered models and | ||
+ | methods and we conduct user experimentations and user evaluations of the algorithms and platforms we develop. | ||
===== Scalable Software Systems ===== | ===== Scalable Software Systems ===== | ||
+ | With the generalisation of multi-cores and distributed computing resources, programmers face new software | ||
+ | parallelisation, adaptability, and scaling-up complexities. SPARKS research in that field is grounded on | ||
+ | a long-standing experience in the domains of scientific workflow systems that facilitate distributed computing | ||
+ | infrastructures exploitation by non-expert users, scalable and secure software composition techniques, and | ||
+ | adaptation to heterogeneous and dynamic execution environments. In addition, part of the activity | ||
+ | complements the research theme FORUM theme with concerns related to large-scale | ||
+ | distributed data repositories integration. | ||