Christos Tryfonopoulos

Publish/subscribe functionality in IR environments using structured overlay networks

We study the problem of offering publish/subscribe functionality on top of structured overlay networks using data models and languages from IR. We show how to achieve this by extending the distributed hash table Chord and present a detailed experimental evaluation of our proposals.

Selective information dissemination in P2P networks: problems and solutions

We study the problem of selective dissemination of information in P2P networks. We present our work on data models and laiguages for textual information dissemination and discuss a relemnt P2P architecture that motivates our efforts. We also survey our results on the computational complexity of three related algorithmic problems (query satisfiability, entailment and filtering) and present efficient algorithms for the most crucial of these problems (filtering). Finally, we discuss the features of P2P-DIET, a super-peer system we have implemented at the Technical Lniversity of Crete, that realizes our vision and is able to support both ad-hoc querying and selective information dissemination scenarios in a P2P framework.

Information Alert in Distributed Digital Libraries: The Models, Languages, and Architecture of DIAS

This paper presents DIAS, a distributed alert service for digital libraries, currently under development in project DIET. We first discuss the models and languages for expressing user profiles and notifications. Then we present the data structures, algorithms and protocols that underly the peer-to-peer agent architecture of DIAS.

LibraRing: an architecture for distributed digital libraries based on DHTs

We present a digital library architecture based on distributed hash tables. We discuss the main components of this architecture and the protocols for offering information retrieval and information filtering functionality. We present an experimental evaluation of our proposals.

Filtering algorithms for information retrieval models with named attributes and proximity operators

In the selective dissemination of information (or publish/subscribe) paradigm, clients subscribe to a server with continuous queries (or profiles) that express their information needs. Clients can also publish documents to servers. Whenever a document is published, the continuous queries satisfying this document are found and notifications are sent to appropriate clients. This paper deals with the filtering problem that needs to be solved effciently by each server: Given a database of continuous queries db and a document d, find all queries q ∈ db that match d. We present data structures and indexing algorithms that enable us to solve the filtering problem efficiently for large databases of queries expressed in the model AWP which is based on named attributes with values of type text, and word proximity operators.

Information filtering and query indexing for an information retrieval model

In the information filtering paradigm, clients subscribe to a server with continuous queries or profiles that express their information needs. Clients can also publish documents to servers. Whenever a document is published, the continuous queries satisfying this document are found and notifications are sent to appropriate clients. This article deals with the filtering problem that needs to be solved efficiently by each server: Given a database of continuous queries db and a document d, find all queries q ∈ db that match d. We present data structures and indexing algorithms that enable us to solve the filtering problem efficiently for large databases of queries expressed in the model AWP. AWP is based on named attributes with values of type text, and its query language includes Boolean and word proximity operators.

Approximate Information Filtering in Peer-to-Peer Networks

Most approaches to information filtering taken so far have the underlying hypothesis of potentially delivering notifications from every information producer to subscribers. This exact publish/subscribe model creates an efficiency and scalability bottleneck, and might not even be desirable in certain applications. The work presented here puts forward MAPS, a novel approach to support approximate information filtering in a peer-to-peer environment. In MAPS a user subscribes to and monitors only carefully selected data sources, and receives notifications about interesting events from these sources only. This way scalability is enhanced by trading recall for lower message traffic. We define the protocols of a peer-to-peer architecture especially designed for approximate information filtering, and introduce new node selection strategies based on time series analysis techniques to improve data source selection. Our experimental evaluation shows that MAPS is scalable; it achieves high recall by monitoring only few data sources.

Distributed Evaluation of Continuous Equi-join Queries over Large Structured Overlay Networks

We study the problem of continuous relational query processing in Internet-scale overlay networks realized by distributed hash tables. We concentrate on the case of continuous two-way equi-join queries. Joins are hard to evaluate in a distributed continuous query environment because data from more than one relations is needed, and this data is inserted in the network asynchronously. Each time a new tuple is inserted, the network nodes have to cooperate to check if this tuple can contribute to the satisfaction of a query when combined with previously inserted tuples. We propose a series of algorithms that initially index queries at network nodes using hashing. Then, they exploit the values of join attributes in incoming tuples to rewrite the given queries into simpler ones, and reindex them in the network where they might be satisfied by existing or future tuples. We present a detailed experimental evaluation in a simulated environment and we show that our algorithms are scalable, balance the storage and query processing load and keep the network traffic low.

Rewiring strategies for semantic overlay networks

Semantic overlay networks cluster peers that are semantically, thematically or socially close into groups, by means of a rewiring procedure that is periodically executed by each peer. This procedure establishes new connections to similar peers and disregards connections to peers that are dissimilar. Retrieval effectiveness is then improved by exploiting this information at query time (as queries may address clusters of similar peers). Although all systems based on semantic overlay networks apply some rewiring technique, there is no comprehensive study showing the effect of rewiring on system’s performance. In this work, a framework for studying the attribution of rewiring strategies in semantic overlay networks is proposed. A generic approach to rewiring is presented and several variants of this approach are reviewed and evaluated. We show how peer organisation is affected by the different design choices of the rewiring mechanism and how these choices affect the performance of the system overall (both in terms of communication overhead and retrieval effectiveness). Our experimental evaluation with real-word data and queries confirms the dependence between rewiring strategies and retrieval performance, and gives insights on the trade-offs involved in the selection of a rewiring strategy.

Data Models and Languages for Agent-Based Textual Information Dissemination

We define formally the data models WP, AWP and AWPS especially designed for the dissemination of textual information by distributed agent systems using communication languages such as KQML and FIPA-ACL. We also define the problems of satisfaction and filtering and point out that these problems are fundamental for the deployment of our models in distributed agent architectures appropriate for information dissemination. One such architecture currently under development in project DIET is sketched in some detail in this paper. Finally, we present algorithms for the problems of satisfaction and filtering, prove the correctness of these algorithms, and calculate their computational complexity.