International audience

Given an arbitrary group of computers, how to identify abnormal changes in their communication pattern? How to assess if the absence of some communications is normal or due to a failure? How to distinguish local from global events when communication data are extremely sparse and volatile? Existing approaches for anomaly detection in interaction streams, focusing on edge, nodes or graphs, lack flexibility to monitor arbitrary communication topologies. Moreover, they rely on structural features that are not adapted to highly sparse settings. In this work, we introduce MAD, a novel Multi-scale Anomaly Detection algorithm that (i) allows to query for the normality/abnormality state of an arbitrary group of observed/non-observed communications at a given time; and (ii) handles the highly sparse and uncertain nature of interaction data through a scoring method that is based on a novel probabilistic and multi-scale analysis of sub-graphs. In particular, MAD is (a) flexible: it can assess if any time-stamped subgraph is anomalous, making edge, node and graph anomalies particular instances; (b) interpretable: its multi-scale analysis allows to characterize the scope and nature of the anomalies; (c) efficient: given historical data of length N and M observed/non-observed communications to analyze, MAD produces an anomaly score in O (NM); and (d) effective: it significantly outperforms state-of-the-art alternatives tailored for edge, node or graph anomalies.

A large amount of linked data is now available, but to retrieve knowledge from these data, queries have to be formulated using formal query languages. While expressive query languages are developed, their use by end users, generally not familiar with formal languages, is limited. Keyword-based search is considered as a convenient and intuitive way for users to express their information needs. Keyword search over structured data is thus an interesting alternative but which raises challenging issues. The main challenge is to determine the meaning of a keyword query in order to translate it into a target formal query language, SPARQL in our case. In this paper, we address this challenge and propose a novel approach that relies on user interactions to determine the correct interpretation of the keyword query. The principle is to first ask the user to define a coarse keyword query, then to suggest candidate interpretations expressed in an explicit, thus unambiguous, and human readable form. Once the correct interpretation has been selected, the query may be refined with aggregate functions and comparatives. Experiments conducted on a large knowledge base show the effectiveness and the efficiency of the proposed approach.

AGGREGO SEARCH offers a novel keyword-based query solu-tion for end users in order to retrieve precise answers from se-mantic data sources. Contrary to existing approaches, AGGREGO SEARCH suggests grammatical connectors from natural languages during the query formulation step in order to specify the meaning of each keyword, thus leading to a complete and explicit definition of the intent of the search. An example of such a query is name of person at the head of company and author of article about “busi-ness intelligence". In order to help users formulate such connected keywords queries, a specific autocompletion strategy has been de-veloped. A translation of the user keyword query into SPARQL is performed on-the-fly during the interactive query construction pro-cess. For this demonstration, we show how AGGREGO SEARCH has been integrated on top of a mediation system to let users intu-itively define explicit and precise keyword queries in order to ex-tract knowledge distributed in heterogeneous large semantic data sources. 1.

In this demonstration we present a complete fuzzy-set-based approach to preference queries that tackles the two main questions raised by the introduction of flexibility and personalization when querying relational databases: i) how to efficiently execute preference queries? and, ii) how to help users define preferences and queries? As an answer to the first question, we propose PostgreSQL_f, a module implemented on top of PostgreSQL to handle fuzzy queries. To answer the second question, we propose ReqFlex an intuitive user interface to the definition of preferences and the construction of fuzzy queries.