Data sharing in DHT based P2P systems

International audienceThe evolution of peer-to-peer (P2P) systems triggered the building of large scale distributed applications. The main application domain is data sharing across a very large number of highly autonomous participants. Building such data sharing systems is particularly challenging because of the "extreme" characteristics of P2P infrastructures: massive distribution, high churn rate, no global control, potentially untrusted participants... This article focuses on declarative querying support, query optimization and data privacy on a major class of P2P systems, that based on Distributed Hash Table (P2P DHT). The usual approaches and the algorithms used by classic distributed systems and databases forproviding data privacy and querying services are not well suited to P2P DHT systems. A considerable amount of work was required to adapt them for the new challenges such systems present. This paper describes the most important solutions found. It also identies important future research trends in data management in P2P DHT systems

The essence of P2P: A reference architecture for overlay networks

The success of the P2P idea has created a huge diversity of approaches, among which overlay networks, for example, Gnutella, Kazaa, Chord, Pastry, Tapestry, P-Grid, or DKS, have received specific attention from both developers and researchers. A wide variety of algorithms, data structures, and architectures have been proposed. The terminologies and abstractions used, however, have become quite inconsistent since the P2P paradigm has attracted people from many different communities, e.g., networking, databases, distributed systems, graph theory, complexity theory, biology, etc. In this paper we propose a reference model for overlay networks which is capable of modeling different approaches in this domain in a generic manner. It is intended to allow researchers and users to assess the properties of concrete systems, to establish a common vocabulary for scientific discussion, to facilitate the qualitative comparison of the systems, and to serve as the basis for defining a standardized API to make overlay networks interoperable

Proof-of-Concept Application - Annual Report Year 1

In this document the Cat-COVITE Application for use in the CATNETS Project is introduced and motivated. Furthermore an introduction to the catallactic middleware and Web Services Agreement (WS-Agreement) concepts is given as a basis for the future work. Requirements for the application of Cat-COVITE with in catallactic systems are analysed. Finally the integration of the Cat-COVITE application and the catallactic middleware is described. --Grid Computing

Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data

In the Internet of Things (IoT), Cyber-Physical Systems (CPS), and sensor technologies huge and variety of streaming sensor data is generated. The unification of streaming sensor data is a challenging problem. Moreover, the huge amount of raw data has implied the insufficiency of manual and semi-automatic annotation and leads to an increase of the research of automatic semantic annotation. However, many of the existing semantic annotation mechanisms require many joint conditions that could generate redundant processing of transitional results for annotating the sensor data using SPARQL queries. In this paper, we present an Incremental Clustering Driven Automatic Annotation for IoT Streaming Data (IHC-AA-IoTSD) using SPARQL to improve the annotation efficiency. The processes and corresponding algorithms of the incremental hierarchical clustering driven automatic annotation mechanism are presented in detail, including data classification, incremental hierarchical clustering, querying the extracted data, semantic data annotation, and semantic data integration. The IHCAA-IoTSD has been implemented and experimented on three healthcare datasets and compared with leading approaches namely- Agent-based Text Labelling and Automatic Selection (ATLAS), Fuzzy-based Automatic Semantic Annotation Method (FBASAM), and an Ontology-based Semantic Annotation Approach (OBSAA), yielding encouraging results with Accuracy of 86.67%, Precision of 87.36%, Recall of 85.48%, and F-score of 85.92% at 100k triple data

M-Grid : A distributed framework for multidimensional indexing and querying of location based big data

The widespread use of mobile devices and the real time availability of user-location information is facilitating the development of new personalized, location-based applications and services (LBSs). Such applications require multi-attribute query processing, handling of high access scalability, support for millions of users, real time querying capability and analysis of large volumes of data. Cloud computing aided a new generation of distributed databases commonly known as key-value stores. Key-value stores were designed to extract value from very large volumes of data while being highly available, fault-tolerant and scalable, hence providing much needed features to support LBSs. However complex queries on multidimensional data cannot be processed efficiently as they do not provide means to access multiple attributes. In this thesis we present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set of nodes in a P-Grid overlay network which provides fault-tolerance and efficient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to efficiently manage multi-dimensional data in a key-value store. We propose algorithms to dynamically process range and k nearest neighbor (kNN) queries on linearized values. This removes the overhead of maintaining a separate index table. Our approach is completely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that MGrid achieves a performance improvement of three orders of magnitude in comparison to MapReduce and four times to that of MDHBase scheme --Abstract, pages iii-iv

Unifying Framework for Building Social Computing Applications

There have been a number of frameworks and models developed to support different aspects of social computing. Some were developed to deal with online interaction through the application of computer-mediated communications tools, whereas others such as social network analyses and reputation systems were more specific in their focus. While these methodologies are interrelated, current social computing research has dealt with them as separate aspects. This paper presents a comprehensive framework for social computing that aims at integrating all these three aspects into a unified model so that discovery and exploration of community members are not only made possible, but also optimized. The novelty of the proposed approach stems from: (1) integration of social interaction, social network analysis and social reputation domains; (2) incorporating many flexible and practical features such as individual- and group-level perceptions of trust in social relations for different social contexts; and (3) using path-related algorithms for selection and discovery of target community members. A description of the proposed model and its implementation are presented. This research is expected to assist online community members to make decisions that facilitate the discovery of people and their connections while promoting increased awareness of community structure and information exposure