A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

Recent developments in the storage, retrieval, and manipulation of large text files are described. The text analysis problem is examined, and modern approaches leading to the identification and retrieval of selected text items in response to search requests are discussed.

https://science.sciencemag.org/content/sci/253/5023/974.full.pdf

Developments in automatic text retrieval.

Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.

/pdf/a-comprehensive-survey-on-fog-computing-state-of-the-art-and-4fbh4oy5fy.pdf

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

The Health Insurance Portability and Accountability Act, also known as HIPAA, was first delivered to congress in 1996 and consisted of just two Titles. It was designed to protect health insurance coverage for workers and their families while between jobs. It establishes standards for electronic health care transactions and addresses the issues of privacy and security when dealing with Protected Health Information (PHI). HIPAA is applicable only in the United States of America.

The Health Insurance Portability and Accountability Act.

Web-Based Education for All: A Tool for Development Adaptive Courseware.

Recent research on the learning process has shown that students tend to learn in different ways and that they prefer to use different teaching resources as well. Many researchers agree on the fact that learning materials shouldnpsilat just reflect of the teacherpsilas style, but should be designed for all kinds of students and all kind of learning styles. Even though they agree on the importance of applying these learning styles to different learning systems, various problems still need to be solved, such as matching teaching contents with the student's learning style. In this paper, we describe the design of a personalized teaching environment that is based on an adaptive taxonomy using Felder and Silverman's learning styles and which is combined with the selection of the appropriate teaching strategy and the appropriate electronic media. Students are able to learn and to efficiently improve their learning process with such method.

/pdf/student-learning-styles-adaptation-method-based-on-teaching-198ovqfkml.pdf

Student Learning Styles Adaptation Method Based on Teaching Strategies and Electronic Media

Recent results in artificial intelligence research are of prime interest in various fields of computer science; in particular we think information retrieval may benefit from significant advances in this approach. Expert systems seem to be valuable tools for components of information retrieval systems related to semantic inference. The query component is the one we consider in this paper. IOTA is the name of the resulting prototype presented here, which is our first step toward what we call an intelligent system for information retrieval . After explaining what we mean by this concept and presenting current studies in the field, the presentation of IOTA begins with the architecture problem, that is, how to put together a declarative component, such as an expert system, and a procedural component, such as an information retrieval system. Then we detail our proposed solution, which is based on a procedural expert system acting as the general scheduler of the entire query processing. The main steps of natural language query processing are then described according to the order in which they are processed, from the initial parsing of the query to the evaluation of the answer. The distinction between expert tasks and nonexpert tasks is emphasized. The paper ends with experimental results obtained from a technical corpus, and a conclusion about current and future developments.

A prototype of an intelligent system for information retrieval: IOTA

In service-oriented computing, a user usually needs to locate a desired service for: (i) fulfilling her requirements or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the Internet, locating a service online may not be appropriate from the performance perspective, especially in large Internet-based service repositories. Instead, services usually need to be clustered according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper, we propose to cluster data providing (DP) services using a refined fuzzy C-means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. In addition, we introduce some operations for managing service clusters, when new services emerge or existing services disappear or become unavailable. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly. We have prototyped our approach and the source code is freely available on the web. We have evaluated our clustering approach in different settings and the results are very promising.

Data Providing Services Clustering and Management for Facilitating Service Discovery and Replacement

Cloud computing has recently emerged as a new computing paradigm enabling on-demand and scalable provision of resources, platforms and software as services. In order to satisfy different storage requirements, cloud applications usually need to access and interact with different relational and NoSQL data stores having heterogeneous APIs. This APIs heterogeneity induces two main problems. First it ties cloud applications to specific data stores hampering therefore their migration. Second, it requires developers to be familiar with different APIs. In this paper, we propose a generic resources model defining the different concept used in each type of data store. These resources are managed by ODBAPI a streamlined and a unified REST API enabling to execute CRUD operations on different NoSQL and relational databases. ODBAPI decouples cloud applications from data stores alleviating therefore their migration. Moreover it relieves developers task by removing the burden of managing different APIs.

ODBAPI: A Unified REST API for Relational and NoSQL Data Stores

This paper reports on the main results of a specific action on mobile databases conducted by CNRS in France from October 2001 to December 2002. The objective of this action was to review the state of progress in mobile databases and identify major research directions for the French database community. Rather than provide a survey of all important issues in mobile databases, this paper gives an outline of the directions in which the action participants are now engaged, namely: copy synchronization in disconnected computing, mobile transactions, database embedded in ultra-light devices, data confidentiality, P2P dissemination models and middleware adaptability.

/pdf/mobile-databases-a-selection-of-open-issues-and-research-2i2sd8s9lp.pdf

Bruno Defude

Papers

Student Learning Styles Adaptation Method Based on Teaching Strategies and Electronic Media

A prototype of an intelligent system for information retrieval: IOTA

Data Providing Services Clustering and Management for Facilitating Service Discovery and Replacement

ODBAPI: A Unified REST API for Relational and NoSQL Data Stores

Mobile databases: a selection of open issues and research directions