Georgios John Fakas

A peer to peer (P2P) architecture for dynamic workflow management

Abstract This paper presents the architecture of a novel Peer to Peer (P2P) workflow management system. The proposed P2P architecture is based on concepts such as a Web Workflow Peers Directory (WWPD) and Web Workflow Peer (WWP). The WWPD is an active directory system that maintains a list of all peers (WWPs) that are available to participate in Web workflow processes. Similar to P2P systems such as Napster and Gnutella, it allows peers to register with the system and offer their services and resources to other peers over the Internet. Furthermore, the architecture supports a novel notification mechanism to facilitate distributed workflow administration and management. Employing P2P principles can potentially simplify the workflow process and provide a more open, scalable process model that is shared by all workflow participants. This would enable for example a WWP to connect directly to another without going through an intermediary, currently represented by the workflow process management server. P2P workflow becomes more efficient as the number of peers performing the same role increases. Available peers can be discovered dynamically from the WWPD. The few currently existing P2P based workflow systems fail to utilise state of the art Web technologies such as Web Services. In contrast, using the approach described here it is possible to expose interoperable workflow processes over the Internet as services. A medical consultation case study is used to demonstrate the proposed system.

The electronic laboratory journal: a collaborative and cooperative learning environment for web-based experimentation

Numerous tools have been developed for supporting the collaboration between students in education, tools that mainly include facilities for sharing documents and enabling discussions. However, these environments do not emphasize the use of facilities that sustain collaborative work in the framework of remote experimentation carried out by a group of students located at different places. The Electronic Laboratory Journal (eJournal) paradigm proposed in this paper is a collaborative and cooperative environment for Web-based experimentation in engineering education. The eJournal enhances the traditional laboratory journal, by providing a group of students with Web-based tools to collect, annotate, organize and share the data chunks necessary to complete their experimentation assignments. The data chunks, called fragments, may be composed of numerous objects of any format, such as text, images, graphics, manuscripts, measurement logs or experimental results. Fragments can be uploaded from local disks or imported from Web components. The eJournal also handles the submission of results to the educators and facilitates remote supervision, assistance and tutoring of the students. The eJournal paradigm is currently assessed at the School of Engineering, the École Polytechnique Fédérale de Lausanne (EPFL), in the framework of hands-on experimentation activities focusing on remote manipulation of real setups and Web-based simulation. This paper presents the eJournal environment, its application and its evaluation as an enabling Web-based application for flexible learning.

A novel keyword search paradigm in relational databases: Object summaries

This paper introduces a novel keyword search paradigm in relational databases, where the result of a search is an Object Summary (OS). An OS summarizes all data held about a particular Data Subject (DS) in a database. More precisely, it is a tree with a tuple containing the keyword(s) as a root and neighboring tuples as children. In contrast to traditional relational keyword search, an OS comprises a more complete and therefore semantically meaningful set of information about the enquired DS. The proposed paradigm introduces the concept of Affinity in order to automatically generate OSs. More precisely, it investigates and quantifies the Affinity of relations (i.e. Affinity) and their attributes (i.e. Attribute Affinity) in order to decide which tuples and attributes to include in the OS. Experimental evaluation on the TPC-H and Northwind databases verifies the searching quality of the proposed paradigm on both large and small databases; precision, recall, f-score, CPU and space measures are presented.

Size- l object summaries for relational keyword search

A previously proposed keyword search paradigm produces, as a query result, a ranked list of Object Summaries (OSs). An OS is a tree structure of related tuples that summarizes all data held in a relational database about a particular Data Subject (DS). However, some of these OSs are very large in size and therefore unfriendly to users that initially prefer synoptic information before proceeding to more comprehensive information about a particular DS. In this paper, we investigate the effective and efficient retrieval of concise and informative OSs. We argue that a good size-l OS should be a stand-alone and meaningful synopsis of the most important information about the particular DS. More precisely, we define a size-l OS as a partial OS composed of l important tuples. We propose three algorithms for the efficient generation of size-l OSs (in addition to the optimal approach which requires exponential time). Experimental evaluation on DBLP and TPC-H databases verifies the effectiveness and efficiency of our approach.

Automated generation of object summaries from relational databases: A novel keyword searching paradigm

This paper introduces a novel keyword searching paradigm in relational databases (DBs), where the result of a search is a ranked set of object summaries (OSs). An OS summarizes all data held about a data subject (DS) in the database. More precisely, it is a tree with a tuple containing the keyword as a root and neighboring tuples as children. In contrast to traditional relational keyword search (R-KwS), an OS comprises a more complete and therefore semantically meaningful set of information about the enquired DS. The proposed paradigm is based on two key concepts: Affinity and Importance. The system investigates and quantifies the Affinity of relations in order to automatically create OSs and OS importance (Im(OS)) in order to rank them. Im(OS)s considers the weight (i.e. pagerank) of tuples, Affinity and size of OS. Experimental evaluation on TPC-H and Northwind DBs so far verifies the searching quality of the proposed paradigm.

Ranking of Object Summaries

A previously proposed Keyword Search paradigm produces, as a query result, a ranked list of Object Summaries (OSs); each OS summarizes all data held in a relational database about a particular Data Subject (DS). This paper further investigates the ranking of OSs and their tuples as to facilitate (1) the top-k ranking of OSs and also (2) the generation of partial size-l OSs (i.e. comprised of the l most important tuples). Therefore, a global Importance score for each tuple of the database (denoted as Im(ti)) is investigated and quantified. For this purpose, ValueRank (an extension of ObjectRank) is introduced which facilitates the estimation of scores for arbitrary databases (in contrast to PageRank-style techniques that are only effective on bibliographic databases). In addition, a variation of Combined functions are investigated for assigning an Importance score to an OS (denoted as Im(OS)) and a local Importance score of their tuples (denoted as Im(OS, ti)). Preliminary experimental evaluation on DBLP and Northwind Databases is presented.

Versatile Size-

The Object Summary (OS)is a recently proposed tree structure, which summarizes all data held in a relational database about a data subject. An OS can potentially be very large in size and therefore unfriendly for users who wish to view synoptic information about the data subject. In this paper, we investigate the effective and efficient retrieval of concise and informative OS snippets (denoted as size-l OSs). We propose and investigate the effectiveness of two types of size- l OSs, namely size- l OS (t)s and size-l OS (a)s that consist of l tuple nodes and l attribute nodes respectively. For computing size-l OSs, we propose an optimal dynamic programming algorithm, two greedy algorithms and preprocessing heuristics. By collecting feedback from real users (e.g., from DBLP authors), we assess the relative usability of the two different types of snippets, the choice of the size- l parameter, as well as the effectiveness of the snippets with respect to the user expectations. In addition, via thorough evaluation on real databases, we test the speed and effectiveness of our techniques.

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This paper presents a novel approach for extracting personal data and automatically generating Personal Data Reports (PDRs) from relational databases. Such PDRs can be used among other purposes for compliance with Subject Access Requests of Data Protection Acts. Two methodologies with different usability characteristics are introduced: (1) the GDS Based Method and (2) the By Schema Browsing Method. The proposed methdologies combine the use of graphs and query languages for the construction of PDRs. The novelty of these methodologies is that they do not require any prior knowledge of either the database schema or of any query language by the users. An optimisation algorithm is proposed that employs Hash Tables and reuses already found data. We conducted several queries on two standard benchmark databases (i.e. TPC-H and Microsoft Northwind) and we present the performance results.

Object Summariesfor Relational Keyword Search

This paper proposes a model for intelligent navigation through multi-contextual information that could form electronic roads in the information society. This paper aims to address the problem of electronic information roads, define their notion and the technical form they can take as well as present the tools developed for implementing such a system. The main objective of the proposed model is to give the traveler the capability of exploring the information space in a natural way where the information offered will remain continuously interesting. The system offers links to information in a dynamic and adaptive way. This is achieved by employing intelligent navigation techniques, which combine user profiling and meta-data. Electronic roads emphasize the presentation of multi-contextual information, i.e., information that is semantically related but of different nature at different locations and time. An electronic road is the user’s navigation path through a series of information units. Information units are the building blocks of the available cultural information content.

Automated Generation of Personal Data Reports from Relational Databases

This paper describes the Cultural Journeys in the Information Society (CJIS) project. CJIS project is an INCO project (973324) funded by the ECC and its aims to address the problem of electronic information roads at various levels and to develop a prototype system of such roads. The prototype will concentrate at first on cultural and historical information and the social relevance of such roads in education and training. This paper defines the notion of electronic information roads and explores the technical form which these roads can take. An electronic road is the users navigation path through a multimedia environment. More precisely, it is a series of links to the systems information units (IU) the user chooses to follow through. IUs are the building blocks of the available information content and consist of the actual data (e.g. segment of video, image, sound or text) with an attached metadata index (semantic or system). The system produces a number of dynamic links that point the user to new information units. These dynamic links are based on the IUs semantic nature and the users profile.