Abstract

n this paper, we have proposed speculative locking (SL) protocols to improve the performance of distributed databasesystems (DDBSs) by trading extra processing resources. In SL, a transaction releases the lock on the data object whenever itproduces corresponding after-image during its execution. By accessing both before and after-images, the waiting transaction carriesout speculative executions and retains one execution based on the termination (commit or abort) mode of the preceding transactions.By carrying out multiple executions for a transaction, SL increases parallelism without violating serializability criteria. Under the naiveversion of SL, the number of speculative executions of the transaction explodes with data contention. By exploiting the fact that asubmitted transaction is more likely to commit than abort, we propose the SL variants that process transactions efficiently bysignificantly reducing the number of speculative executions. The simulation results indicate that even with manageable extraresources, these variants significantly improve the performance over two-phase locking in the DDBS environments where transactionsspend longer time for processing and transaction-aborts occur frequently.

Abstract

The blocking phenomena in two-phase commit (2PC) reduces the availability of the system as the blocked transactionskeep all the resources until the recovery of the coordinator. The three-phase commit (3PC) protocol involves an extra roundof message transmission to resolve the blocking problem. In this paper, we propose a backup commit (BC) protocol to reducethe blocking problem by attaching multiple backup sites to the coordinator site. In BC, after receiving responses from theparticipants, the coordinator quickly communicates the final decision to the backup sites, before it sends the final decision tothe participants. When blocking occurs, the participant sites can terminate the transaction by consulting a backup site of thecoordinator. The BC protocol resolves the blocking in most of the coordinator site failures without involving an expensivecommunication cycle as in 3PC. The simulation experiments indicate that the throughput performance of BC is close to 2PC.2002 Elsevier Science B.V. All rights reserved.

Abstract

Distributed locking is commonly adopted for performing concurrency control in distributed systems. It incorporatesadditional steps for handling deadlocks. This activity is carried out by methods based on wait-for-graphs or probes. The present studyexamines detection of conflicts based on enhanced local processing for distributed concurrency control. In the proposed “edgedetection” approach, a graph-based resolution of access conflicts has been adopted. The technique generates a uniform wait-forprecedence order at distributed sites for transactions to execute. The earlier methods based on serialization graph testing are difficultto implement in a distributed environment. The edge detection approach is a fully distributed approach. It presents a unified techniquefor locking and deadlock detection exercises. The technique eliminates many deadlocks without incurring message overheads

Abstract

Insect pests are a major cause of crop loss globally. Pest management will be effective and efficient if we can predict the occurrence of peak activities of a given pest. Research efforts are going on to understand the pest dynamics by applying analytical and other techniques on pest surveillance data sets. In this study we make an effort to understand pest population dynamics using Neural Networks by analyzing pest surveillance data set of Helicoverpa armigera or Pod borer on chickpea (Cicer arietinum L.) crop. The results show that neural network method successfully predicts the pest attack incidences for one week in advance.

Abstract

In this paper we propose an approach to extract community structures in the Web by considering a community structure as a group of content creators that manifests itself as a set of interlinked pages. We abstract the community structure as a dense bipartite graph (DBG) over a group of Web pages and proposed an algorithm to extract the DBGs from the given data set. Also, a high-level community is abstracted as a DBG over a set of a low-level communities. Using the proposed approach, a community hierarchy can be constructed for the given data set that generalizes a large number of low-level communities into a few highlevel communities. Using the proposed approach, we have extracted a a three-level community hierarchy from 10 GB TPEC (Text PEtrieval Conference) data set. We believe that the extracted community hierarchy facilitates easy analysis of the low-level communities, helps in reorganizing the Web sites, and provides a way to understand the sociology of the Web

Abstract

Indian farming community is facing a multitude of problems to maximize cropproductivity. In spite of successful research on new agricultural practices concerning cropcultivation, the majority of farmers is not getting upper-bound yield due to several reasons. Oneof the reasons is that expert/scientific advice regarding crop cultivation is not reaching farmingcommunity in a timely manner. It is true that India possesses a valuable agricultural knowledgeand expertise. However, a wide information gap exists between the research level and practice.Indian farmers need timely expert advice to make them more productive and competitive. In thispaper, we made an effort to present a solution to bridge the information gap by exploitingadvances in Information Technology (IT). We propose a framework of a cost-effectiveagricultural information dissemination system (AgrIDS) to disseminate expert agricultureknowledge to the farming community to improve the crop productivity. Some of the crucialbenefits of AgrIDS are as follows. It is a scalable system which can be incrementally developedand extended to cover all the farmers (crops) of India in a cost effective manner. It enables thefarmer to cultivate a crop with expertise, as that of an agricultural expert, by disseminating bothcrop and location specific expert advice in a personalized and timely manner. With AgrIDS, thelag period between research effort to practice can be reduced significantly. Finally, the proposedsystem assumes a great importance due to the trend of globalization, as it aims to provide expertadvice which is crucial to for the Indian farmer to harvest different kinds of crop varietiesbased on the demand in the world market.

Abstract

In E-commerce sites, recommendation systems are used torecommend products to their customers. Collaborative filtering (CF) iswidely employed approach to recommend products. In the literature,researchers are making efforts to improve the scalability and onlineperformance of CF. In this paper we propose a graph based approach toimprove the performance of CF. We abstract a neighborhood communityof a given customer through dense bipartite graph (DBG). Given adata set of customer preferences, a group of neighborhood customersfor a given customer is extracted by extracting corresponding DBG.The experimental results on the MovieLens data set show that therecommendation made with the proposed approach matches closelywith the recommendation of CF. The proposed approach possesses apotential to adopt to frequent changes in the product preference data set.