Pierre Morizet-Mahoudeaux

Preserving interactive digital music: a report on the MUSTICA research initiative

The promise of recent technological and legislative developments to facilitate the digital dissemination of music is undermined by the lack of reliable means to preserve accurate copies of digital files: music that can be easily transmitted and played back today may not be retrievable tomorrow. Preserving interactive music compositions is particularly problematic, as their performance typically relies on a variety of specialized components. We describe the planned research activities of MUSTICA, an international team of archivists, information scientists, and musicologists that seeks to develop tools to guide the preservation and presentation of interactive digital musical compositions in accordance with the standards and strategies for electronic records preservation being developed by MUSTICAs parent research initiative, InterPARES 2.

A hierarchy of network-based knowledge systems

An extension of the method that was used for building and using the network-based knowledge system SUPER is proposed to fully utilize the benefits of this approach in the domain of diagnosing distributed dynamically evolving processes. In the scope of distributed sensor networks, several issues are addressed concerning problems dealing with software architectures, strategies, and properties for efficient sensor data management: (1) how to build links efficiently between the elements of each network-based knowledge base, (2) how to maintain consistency of the whole structure and manage the constraints of domain dependent variables corresponding to sensor data, (3) how to manage and update efficiently the database at run time in order to maintain its consistency and to satisfy a high level of response time performances, and (4) how to propagate the solutions given by a qualitative knowledge base into a knowledge base utilizing sensor data whenever the sensors are out of order. The answers given are based on extending and generalizing the principles that have been defined for SUPER. >

Maintaining consistency of database during monitoring of an evolving process by a knowledge-based system

A method of managing and updating a database of facts for engineering systems that change their states over time is proposed. Plan selection and monitoring of dynamically evolving processes involves: (1) deciding whether or not action is warranted based on information about the current state of the system, (2) choosing an appropriate set of actions to change its state, and (3) inferring the effect of those actions on the systems state. The proposed methodology is based on the assumption that the monitored system follows a logical model. An intuitive definition of this logical model and a short justification of this assumption are presented. The knowledge-acquisition system (Super) that was developed to build a network of rules that represents this logic model and the properties of the knowledge-base structure are briefly presented. A strategy to maintain and update the database is defined. Data are correctly inserted, deleted, or changed by using the definition of consistency based on the network of rules. The case of rules that represent the changes of states of the system in response to external actions is developed. Two solutions are presented, depending on whether the history of the state changes is maintained or not. A looped inference engine is introduced to ensure goals analysis and plan selection according to the past and present data. The strategy is based on assigning a goal a rating depending on two factors: the ratio of the number of supporting facts to a function of the total number of facts in the database, and the relationships of the supporting facts to the goals. >

Indexing and mining audiovisual data

In this paper we present a review of recent research and development works, which have been developed in the domain of indexing and mining audio-visual document. We first present the characteristics of the audio-visual documents and the outcomes of digitising this kind of documents. It raises several important issues concerning the new definition of what is a document, what is indexing and what are the numeric principles and technologies available for performing indexing and mining tasks. The analysis of these issue let us introduce the notion of temporal and multimedia objects, and the presentation of the three steps for indexing multimedia documents. It includes the clear distinction between descriptors and indexing. Finally we introduce the MPEG-7 paradigm, which sets the technical environment for developing indexing applications; Then we shortly review current developments, based on the text mining, the XML-Schema, and the event description interface approaches.

An approach to monitoring and diagnosing engineering processes by a knowledge-based system

Abstract This article presents the approach used for building and using the knowledge-based system SUPER to fulfill several types of requirements corresponding to different engineering-domain contexts. SUPER was developed for maintaining and updating information about an evolving system as its slate changes due to abnormal events such as faults or as a consequence of external actions taken on the system. After analyzing different requirements corresponding to engineering-application domains, the successive steps of the approach are given, mainly based on structuring the knowledge base in the shape of a network at the time of knowledge acquisition and maintaining the database consistent at run time. The first step was to develop a knowledge-acquisition module that uses a logical knowledge model to construct and compile the knowledge base as an AND/OR network and that maintains a consistent network of rules as it incrementally increases. The second step was to develop a looped inference engine to select an ...

Integrating Social Network Data for Empowering Collaborative Systems

Over the past years, online social networks with websites such as Facebook, Twitter or LinkedIn, have become a very important part of our everyday life. These websites are increasingly used for creating, publishing and sharing information by users. This creates a huge amount of information a part of which may match the interests of a given group. However the distributed and protected nature of these information make it difficult for retrieving. In this paper, we present a user-centered approach for aggregating social data of members of a group to promote the collaboration and the sharing of knowledge inside collaborative systems. The members will be able to delegate the proposed system to aggregate their different social profiles and to make available the relevant part of information to other members of the group.

Handling variables in a logical network of rules, revision of consistency controlling method of the plan of action manager SUPER

The problem of using global variables to improve the representation capacities of the SUPER expert system is addressed. The first difficulty is to propagate the domain of definition of the variables along the knowledge base using the AND/OR transitive relations between rules. The second problem is to upgrade the algorithm that has been defined for the consistency maintenance of the knowledge base for propositional logic to the level of logic with global variables. Answers are given to both problems: (1) the principles for chaining rules with global variables are given; (2) tools for computing the propagation of the domain of variables along the knowledge base are defined; (3) consistency definitions are given and tools for maintaining the consistency of the knowledge base as it incrementally increases are proposed.<<ETX>>

A user-centered and group-based approach for social data filtering and sharing

We propose a user-centered & group-based approach for social data filtering & sharing.It aggregates user social data across social networks & extracts relevant information.It also allows users to share their social data and intelligence within groups.We describe an extensible system architecture for implementing our proposed approach.We present a web-based prototype and discuss some findings of a primary test. Social networking sites (SNSs) like Facebook, Google+, Twitter, LinkedIn have become a very important part of our daily life. People are connected to multiple SNSs for networking, communicating, collaborating, sharing and seeking for information. Although, the diversity of current SNSs increases and enriches our online experience, they cause some problems. One of the major issues is that users are often overwhelmed by the huge number of social data. It is even worse as these social data are scattered across disconnected SNSs. To address such problems, we propose a user-centered and group-based approach for social data filtering and sharing. First, it allows users to aggregate their social data from different SNSs and to extract relevant contents. Users explicitly define their interests via specific queries, using information filtering techniques, the system will retrieve new corresponding contents. Second, it is expected to extend its first user-centered purpose by allowing group-based information sharing and management. Users can share some part of their own social data with and collectively define the information organization within their respective groups. To describe further and illustrate our proposed approach, a system architecture and a prototype are also presented in this paper. A primary test was carried out and showed encouraging results confirming the added values of our approach.

An aggregation model of online social networks to support group decision-making

Online social networks, more commonly called social networks, with websites such as Facebook, Twitter or LinkedIn, have become a very important part of our everyday life. An enormous amount of data is increasingly generated by millions of connected users. These data cover lots of personal and social information including users’ profile information, their current topics of interest, mutual relationships and so on. In this paper, we present a new approach for aggregating such available data with the objective of knowledge sharing and group decision support. The proposed system is able to access, gather, filter and integrate relevant information from social networks, more precisely those published by the members of a given group, into a collaborative knowledge system. Gathered information is centralized and thus accessible to other members at a single place. It can also be combined with other types of information like internal collaborative traces (i.e. member interactions, member activities) and be efficiently visualized for supporting group-related decision-making processes.

Fraud Detection on Large Scale Social Networks

The incredible growth of the internet use for all kinds of businesses has generated at the same time an increase of fraudulent activities, which calls for developing new methods and tools for detecting fraud and other crimes against banks and customers. Fraud detection needs to analyze and link data, which are gathered from heterogeneous data repositories, and to address problem solving algorithms optimization and parallelization, new knowledge representation paradigms, association mechanisms for linking data, and graph analysis for clustering and partitioning. We present in this paper the motivation of our study and the first steps of the work. We will focus on the emergence of new coding models based on MapReduce and SQL extensions, and on graphs paths issues.