Abstract

Despite the growing importance of other sectors, agriculture continues to hold the key to the growth of GDP, food security, employment and income in India. Over a period of time, agriculture is undergoing a change towards high value crops, livestock, fisheries etc. Along with it substantial changes have occurred in seed varieties, use of chemical inputs, cultivation practices. Such a process has also culminated in distress conditions in the farm sector. Studies1 undertaken in the states of Andhra Pradesh, Karnataka, Maharastra and Punjab have revealed the serious crisis among the farming community. It is pertinent to note that net income is negative in some states( Narayan Moorthy: 2006) and nearly two thirds of the farmers are frustrated with their profession ( Desh

Abstract

For clustering, cosine similarity method is apopular approach to compute the similarity between twodocument vectors where each document vector consists ofweighted terms. It can be noted that, the value of theweight assigned to the terms influences the similarity value.Generally, the weight for each term is calculated by using thepopular TF-IDF method. In the TF-IDF method, the weightof a term is a function of its frequency in the document andin overall document collection. However, it can be observedthat, TF-IDF weighting method ignores the semantic asso-ciation between the terms. In this paper, we have proposedan improved term weighting method calledBoostWeightbyconsidering the semantic association between the terms. Forthis, we exploit the generalization ability of hierarchicalknowledge repositories such as “Open Web Directory” tosemantically associate the terms. The proposedBoostWeightmethod increases the weight of terms if they have commongeneralized term. The clustering experiments on the realdata-set show thatBoostWeightimproves both entropy andpurity values over traditional term weighting approaches.

Abstract

In this paper we have proposed an improved ap-proach to extract rare association rules. Rare association rulesare the association rules containing rare items. Rare items are lessfrequent items. For extracting rare itemsets, the single minimumsupport (minsup) based approaches like Apriori approach sufferfrom “rare item problem” dilemma. At high minsup value, rareitemsets are missed, and at low minsup value, the number offrequent itemsets explodes. To extract rare itemsets, an effort hasbeen made in the literature in which minsup of each item is fixedequal to the percentage of its support. Even though this approachimproves the performance over single minsup based approaches,it still suffers from “rare item problem” dilemma. If minsup forthe item is fixed by setting the percentage value high, the rareitemsets are missed as the minsup for the rare items becomes closeto their support, and if minsup for the item is fixed by setting thepercentage value low, the number of frequent itemsets explodes.In this paper, we propose an improved approach in which minsupis fixed for each item based on the notion of “support difference”.The proposed approach assigns appropriate minsup values forfrequent as well as rare items based on their item supportsand reduces both “rule missing” and “rule explosion” problems.Experimental results on both synthetic and real world datasetsshow that the proposed approach improves performance overexisting approaches by minimizing the explosion of number offrequent itemsets involving frequent items and without missingthe frequent itemsets involving rare items.

Abstract

Rare association rules (or rules) are the association rules involving rare items (low frequent items). To minerare association rules efficiently, an effort has been made in CFP-growth approach to mine frequent patternsusing multiple minimum support (minsup) values. This approach is an extension of FP-growth approach tomultiple minsup values. This approach involves construction of MIS-tree and generating frequent patternsfrom the MIS-tree. The issue in CFP-growth is constructing the compact MIS-tree because CFP-growth con-siders certain items which will generate neither frequent patterns nor rules. In this paper, we propose an effi-cient approach for constructing the compact MIS-tree. To do so, the proposed approach explores the notions“least minimum support” and “infrequent child node pruning”. Experimental results on both synthetic andreal world datasets show that the proposed approach improves the performance over CFP-growth approach.

Abstract

A read-only transaction (ROT) does not modify anydata. Efforts are being made in the literature toimprove the performance of ROTs without correct-ness and data currency issues. The widely used two-phase locking protocol (2PL) processes the transac-tions without any correctness and data currency issues.However, the performance of 2PL deteriorates withdata contention. Snapshot isolation (SI)-based pro-tocols proposed in the literature improve the perfor-mance of ROTs, but they compromise on correctnessand data currency issues. Speculative locking (SL)protocols are proposed in the literature for improv-ing the performance of ROTs by carrying out spec-ulative executions only for ROTs and following 2PLfor update transactions. In SL-based protocols, up-date transactions are blocked if they conflicting withROTs. In this paper, we have proposed an improvedapproach to improve parallelism among update trans-actions and ROTs by exploiting a new notion called“compensatability”. In this protocol, an ROT whichcan be “compensatable” can complete the executionand carry out compensating operation to incorporatethe effect of conflicting update transactions. As aresult, the parallelism is improved over SL protocolsas the update transactions which are conflicting with‘compensatable’ ROTs need not block. In this paper,we have proposed a protocol by exploiting both “com-pensatability” property of ROTs and speculation. Thesimulation results show that the proposed protocol im-proves the performance by carrying out less number ofspeculative executions. Further, the proposed protocoldoes not violate serializability criteria.

Abstract

A read-only transaction (ROT) does not modify any data. The main issues regarding processing of read-only transactions (ROTs) are correctness, data currency and performance. Even though the popular two-phase locking protocol processes ROTs correctly with no data currency related issues, its performance deteriorates with data contention. To improve the performance of ROTs, snapshot isolation-based approaches have been proposed. Eventhough snapshot isolation-based approaches improve the performance of ROTs, both data currency of ROTs andcorrectness (serializability) are compromised. In this paper, we propose an asynchronous speculative locking protocol (called as ASLR) which improves the performance ofROTs by trading extra processing resources. The simulation results show that ASLR improves the performance of ROTs significantly over two-phase locking and snapshot isolation-based approaches with manageable extra processing resources. The ASLR approach processes ROTs without any data currency and correctness issues.

Abstract

A read-only transaction (ROT) does not modify any data. The main issues regarding processing ROTs are correctness, data currency and perform-ance. Two-phase Locking (2PL) protocol is widely used for concurrency con-trol with serializabilty as correctness criteria. Even though 2PL processes ROTs correctly with no data currency related issues, the performance deteriorates as data contention increases. To improve the performance over 2PL, snapshot iso-lation (SI)-based protocols have been proposed. SI-based protocols process ROTs by reading from a snapshot of the committed data and ignoring the modi-fications produced by the concurrent active transactions. Even though SI-based algorithms improve the performance of ROTs, both data currency of ROTs and correctness (serializability) are compromised. In this paper, we propose an ap-proach to improve the performance of ROTs using speculation without compromising data currency of transactions and correctness. The proposed approach improves the performance of ROTs by trading extra computing re-sources without violating serializability as correctness criteria. The simulation results show that with the proposed protocol the throughput performance is im-proved significantly over 2PL and SI-based approaches with manageable extra resources.

Abstract

In this paper, we explain a personalized agricultural advisory system called eSagu, which has been developed to improve the performance and utilization of agriculture technology and help Indian farmers. In eSagu, rather than visiting the crop in person, the agricultural expert delivers the expert advice at regular intervals (once in one or two weeks) to each farm by getting the crop status in the form of digital photographs and other information. During 2004-06, through eSagu, agricultural expert advices delivered for about 6000 farms covering six crops. The results show that the expert advices helped the farmers to achieve savings in capital investment and improved the crop yield. Mainly, the data warehouse of farm histories has been developed which is providing the crop related information to the agricultural expert in an integrated manner for generating a quality agricultural expert advice. In this paper, after explaining eSagu and its advantages, we discuss how data warehouse of farm histories is enabling agricultural expert to deliver a quality expert advice. We also discuss some research issues to improve the performance of eSagu.

Abstract

A read-only transaction (ROT) does not modify any data. The main issues regarding processing ROTs are correctness, data currency and performance. Two-phase Locking (2PL) protocol is widely used for concur-rency control with serializabilty as correctness criteria. Even though 2PL processes ROTs correctly with no data currency related issues, the performance deteriorates as data contention increases. To improve the performance, an approach has been proposed in the literature by considering “Snapshot Isolation (SI)” as correctness criteria to process ROTs. At SI-level, ROTs are processed by reading from a snapshot of the committed data and ignoring the modifications produced by the concurrent active transactions. Even though SI-based algorithms improve the performance of ROTs, data currency of ROTs and correctness (serializability) are compromised. In this paper, we propose an approach by extending the notion of speculation to improve the performance of ROTs without com-promising data currency of transactions and correctness. Speculation-based approach improves the performance of ROTs by trading extra computing resources without violating serializability as correctness criteria. The simula-tion results show that with the proposed protocol the throughput performance is improved significantly over 2PL and the data currency of ROTs is improved significantly over SI-based ap proaches with manageable extra re-sources.

Abstract

Timely agro-advisory service and efficient risk mitigation mechanisms can provide stableincome to the farmers. To provide timely agro-advisory service, e-Sagu systemhas beendeveloped by IIIT-Hyderabad and Media Lab Asia. It is an IT-based personalized agro-advisorysystemwhich is being developedto provide high-qualitypersonalized (farm-specific) agricultural expert adviceto each and everyfarmin a timely manner at thefarmer’s door-step without farmer asking a question. The results show that significantbenefits have flowntothe farmers interms of reducedinputs andenhancedyield.Regarding risk mitigation, the agri-insurance schemes around the worldwhether anadvanced or a developing nation like India are suffering due to catastrophic losses,covariate risks, asymmetric information and lack of quality data. These problems maypose a serious threat to the survival of the implementing agencies unless the there is aneffective monitoring systemin place, with enough man power and robust infrastructure.In this paper we explain how agri-insurance schemes can be effectively implemented ontop of eSagu system. The agri-insurance scheme can bevery cost-effective as it can useresources and processes of eSagu system. Also, as eSagu provides quality and reliabledata at farmlevel,the problemof asymmetric information can be effectively hedged.Most important, eSagualsoimproves the performance offormal risk mitigationmechanisms through farm-specific agro-advisory and monitoring system.