Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by considering each item in equal value. However, items are actually different in many aspects in a number of real applications, such as retail marketing, network log, etc. The difference between items makes a strong impact on the decision making in these applications. Therefore, traditional ARM cannot meet the demands arising from these applications. By considering the different values of individual items as utilities, utility mining focuses on identifying the itemsets with high utilities. As "downward closure property" doesn't apply to utility mining, the generation of candidate itemsets is the most costly in terms of time and memory space. In this paper, we present a Two-Phase algorithm to efficiently prune down the number of candidates and can precisely obtain the complete set of high utility itemsets. In the first phase, we propose a model that applies the "transaction-weighted downward closure property" on the search space to expedite the identification of candidates. In the second phase, one extra database scan is performed to identify the high utility itemsets. We also parallelize our algorithm on shared memory multi-process architecture using Common Count Partitioned Database (CCPD) strategy. We verify our algorithm by applying it to both synthetic and real databases. It performs very efficiently in terms of speed and memory cost, and shows good scalability on multiple processors, even on large databases that are difficult for existing algorithms to handle.

As scientists expand their models to describe physical phenomena of increasingly large extent, I/O becomes crucial and a system with limited I/O capacity can severely constrain the performance of the entire program.We provide experimental results, performed on an lntel Touehtone Delta and nCUBE 2 I/O system, to show that the performance of existing paralld//O systems can vary by several orders of magnitude as a function of the data access pattern of the parallel program. We then propose a two-phase access strategy, to be implemented in a runtime system, in which the data distribution on computational nodes is decoupled from storage distzibution. Our experimental results show that performance improvements of several orders of magnitude over direct access based data distribution methods can be obtained, and that performance for most data access patterns can be improved to within a factor of 2 of the best performance. Further, the cost of redistribution is a very small fraction of the overall access cost.

Abstract

Abstract-We address the problem of efficient processing of spatio-temporal range queries for moving objects whose whereabouts in time are not known exactly. The fundamental question tackled by such queries is, given a spatial region and a temporal interval, retrieve the objects that were inside the region during the given time-interval. As earlier works have demonstrated, when the (location,time) information is uncertain, syntactic constructs are needed to capture the impact of the uncertainty, along with the corresponding processing algorithms. In this work, we focus on the uncertainty model that represents the whereabouts in-between two known locations as a bead, and an uncertain trajectory is represented as a necklace -a sequence of beads. For each syntactic variant of the range query, we present the respective processing algorithms and, in addition, we propose pruning strategies that speed up the generation of the queries' answers. We also present the experimental observations that quantify the benefits of our proposed methodologies.

Advancements in sequencing technologies have witnessed an exponential rise in the number of newly found enzymes. Enzymes are proteins that catalyze bio-chemical reactions and play an important role in metabolic pathways. Commonly, function of such enzymes is determined by experiments that can be time consuming and costly. Hence, a need for a computing method is felt that can distinguish protein enzyme sequences from those of non-enzymes and reliably predict the function of the former. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been presented. But, these approaches are known to fail for proteins that perform the same function and are dissimilar in their sequence and structure. In this article, we present a supervised machine learning model to predict the function class and sub-class of enzymes based on a set of 73 sequence-derived features. The functional classes are as defined by International Union of Biochemistry and Molecular Biology. Using an efficient data mining algorithm called random forest, we construct a top-down three layer model where the top layer classifies a query protein sequence as an enzyme or non-enzyme, the second layer predicts the main function class and bottom layer further predicts the sub-function class. The model reported overall classification accuracy of 94.87% for the first level, 87.7% for the second, and 84.25% for the bottom level. Our results compare very well with existing methods, and in many cases report better performance. Using feature selection methods, we have shown the biological relevance of a few of the top rank attributes.