Database Engineering

Abstract With the increasing popularity of object-relational technology, it is becoming important to have a methodology which allows designers to exploit the great modelling power of object-oriented conceptual models (OOCMs) and yet which still facilitates ...

VLSI custom design involves the manipulation of large volumes of diverse interrelated data. The need to invest 4 to 30 man years for microprocessor design makes single-designer oriented methodologies infeasible Sch79]. The effort to communicate concepts and data ...

Conventional search engines generally return a long ordered list of results which the users are forced to sift for getting relevant documents. It is envisaged that the search results can be greatly improved by integrating techniques, like ranking, classification and clustering. This paper explores how these three techniques can be integrated into search engine technology to achieve this goal. We introduce KhojYantra, an Integrated MetaSearch Engine with Classification, Clustering and Ranking. Some of the prominent features of the KhojYantra include a better classification scheme to divide the retrieved documents into most relevant, relevant and general groups, its way of clustering the documents and its technique of prioritizing the documents and the clusters according to their relevancy. KhojYantra also provides an easy to browse interface which along with the above techniques enhances the retrieval effectiveness of traditional ranked list. We designed the system as a secondary search engine to reduce the resources necessary to build KhojYantra and to get a larger coverage of the Web. We evaluate the performance of KhojYantra by using the measures, Percentage of Links Followed in the Group, Average Distance Traversed per Document and Documents Visited per Cluster

Energy saving is the most important issue in wireless mobile computing due to power constraints on mobile units. Data broadcasting is the main method of information dissemination in wireless networks as its cost is independent of the number of mobile hosts receiving ...

Data warehousing applications typically involve massive amounts of data that push database management technology to the limit. A scalable architecture is crucial, not only to handle very large amount of data but also to assure interactive response time to the users. Large data warehouses require a very expensive setup, typically based on high-end servers or high-performance clusters. In this paper we propose and evaluate a simple but very effective method to implement a data warehouse using the computers and workstations typically available in large organizations. The proposed approach is called data warehouse striping with approximate query answering (DWS-AQA). The goal is to use the processing and disk capacity normally available in large workstation networks to implement a data warehouse with a very reduced infrastructure cost. As the data warehouse shares computers that are also being used for other purposes, most of the times only a fraction of the computers will be able to execute the partial queries in time. However, as we show in the paper, the approximated answers estimated from partial results have a very small error for most of the plausible scenarios. Moreover, as the data warehouse facts are partitioned in a strict uniform way, it is possible to calculate tight confidence intervals for the approximated answers, providing the user with a measure of the accuracy of the query results. A set of experiments on the TPC-H benchmark database is presented to show the accuracy of DWS-AQA for a large number of scenarios.

Complex queries are becoming commonplace, with the growing use of decision support systems. Decision support queries often have a lot of common sub-expressions within each query, and queries are often run as a batch. Multi query optimization aims at exploiting common sub-expressions, to reduce the evaluation cost of queries, by computing them once and then caching them for future use, both within individual queries and across queries in a batch. In case cache space is limited, the total size of sub-expressions that are worth caching may exceed available cache space. Prior work in multi query optimization involves choosing a set of common sub-expressions that fit in available cache space, and once computed, retaining their results across the execution of all queries in a batch. Such optimization algorithms do not consider the possibility of dynamically changing the cache contents. This may lead to sub-expressions occupying cache space even if they are not used by subsequent queries. The available cache space can be best utilized by evaluating the queries in an appropriate order and changing the cache contents as queries are executed. We present several algorithms that consider these factors, in order to reduce the cost of query evaluation

Este artigo apresenta um framework que resume aspectos relevantes no suporte a awareness em sistemas de groupware. Awareness (ou percepcao) e o conhecimento sobre o grupo e suas atividades passadas e presentes, e constitui ponto vital para o trabalho cooperativo, sem o qual este trabalho fica descoordenado e perde em qualidade e eficiencia. O framework aqui apresentado comporta-se, na verdade, como uma classificacao ampla e unica para os requisitos necessarios a um suporte a awareness adequado. Sobre este framework foram classificadas diversas ferramentas de groupware disponiveis, formando assim uma visao geral das realizacoes neste dominio.

Complex queries are becoming commonplace, with the growing use of decision support systems. Decision support queries often have a lot of common sub-expressions within each query, and queries are often run as a batch. Multi query optimization aims at exploiting common sub-expressions, to reduce the evaluation cost of queries, by computing them once and then caching them for future use, both within individual queries and across queries in a batch. In case cache space is limited, the total size of sub-expressions that are worth caching may exceed available cache space. Prior work in multi query optimization involves choosing a set of common sub-expressions that fit in available cache space, and once computed, retaining their results across the execution of all queries in a batch. Such optimization algorithms do not consider the possibility of dynamically changing the cache contents. This may lead to sub-expressions occupying cache space even if they are not used by subsequent queries. The available cache space can be best utilized by evaluating the queries in an appropriate order and changing the cache contents as queries are executed. We present several algorithms that consider these factors, in order to reduce the cost of query evaluation

Energy saving is the most important issue in wireless mobile computing due to power constraints on mobile units. Data broadcasting is the main method of information dissemination in wireless networks as its cost is independent of the number of mobile hosts receiving ...

... 171 Ceri, S., Fraternali, P.,1997, Database Applications with Objects and Rules, Addison-Wesley. ... 2, Prentice Hall, New Jersey. [20] Pugh, S., 199 1, Total Design: Integrated Methods for Successful Product Engineering, Addison-Wesley Publishing company. ...

Xiangdong An†‡and Dawn Jutla † †Dept. of Finance, Info. Sys., and Management Sci. ... Saint Mary's University Halifax, NS B3H 3C3, Canada {xan,dawn.jutla}@smu.ca ... Nick Cercone ‡ ‡Faculty of Computer Science Dalhousie University Halifax, NS B3H 1W5, Canada ...

... Dept. IIT Delhi Hauz Khas, New Delhi-16 vkgoyal@cse.iitd.ac.in Shyam K. Gupta Comp. Sci. & Engg. Dept. IIT Delhi Hauz Khas, New Delhi-16 skg@cse.iitd.ac.in Anand Gupta Comp. Sci. & Engg. Dept. NSIT Delhi Sector-3, Dwarka, New Delhi anand@coe.nsit.ac.in Abstract ...