11532289
Computing Complex Iceberg Cubes by Multiway Aggregation and Bounding
Iceberg cubing is a valuable technique in data warehouses. The efficiency of iceberg cube computation comes from efficient aggregation and effective pruning for constraints. In advanced applications, iceberg constraints are often non-monotone and complex, for example, “Average cost in the range [δ 1, δ 2] and standard deviation of cost less than β”. The current cubing algorithms either are efficient in aggregation but weak in pruning for such constraints, or can prune for non-monotone constraints but are inefficient in aggregation. The best algorithm of the former, Star-cubing, computes aggregations of cuboids simultaneously but its pruning is specific to only monotone constraints such as “COUNT(*) ≥ δ”. In the latter case, the Divide and Approximate pruning technique can prune for non-monotone constraints but is limited to bottom-up single-group aggregation. We propose a solution that exhibits both efficiency in aggregation and generality and effectiveness in pruning for complex constraints. Our bounding techniques are as general as the Divide and Approximate pruning techniques for complex constraints and yet our multiway aggregation is as efficient as Star-cubing.
29730920
Translating update operations from relational to object-oriented databases
Abstract In migrating a legacy relational database system to the object-oriented (OO) platform, when database migration completes, application modules are to be migrated, where embedded relational database operations are mapped into their OO correspondents. In this paper we study mapping relational update operations to their OO equivalents, which include UPDATE 1 , INSERT and DELETE operations. Relational update operation translation from relational to OO faces the touchy problem of transformation from a value-based relationship model to a reference-based model and maintaining the relational integrity constraints. Moreover, with a relational database where inheritance is expressed as attribute value subset relationship, changing of some attribute values may lead to the change of the position of an object in the class inheritance hierarchy, which we call object migration. Considering all these aspects, algorithms are given mapping relational UPDATE, INSERT and DELETE operations to their OO correspondents. Our work emphasize in examining the differences in the representation of the source schema's semantics resulting from the translation process, as well as differences in the inherent semantics of the two models.
30931074
A Distributed Object Database's Basic Design Issues
8280671
Information-Based Classification by Aggregating Emerging Patterns
Emerging patterns (EPs) are knowledge patterns capturing contrasts between data classes. In this paper, we propose an information-based approach for classification by aggregating emerging patterns. The constraint-based EP mining algorithm enables the system to learn from large-volume and high-dimensional data; the new approach for selecting representative EPs and efficient algorithm for finding the EPs renders the system high predictive accuracy and short classification time. Experiments on many benchmark datasets show that the resulting classifiers have good overall predictive accuracy, and are often also superior to other state-of-the-art classification systems such as C4.5, CBA and LB.
717712
Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n--dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.
1166438
Building Behaviour Knowledge Space to Make Classification Decision
CAEP, namely Classification by Aggregating Emerging Patterns, builds classifiers from Emerging Patterns (EPs). EPs mined from the training data of a class are distinguishing features of the class. To classify a test instance t, the scores by aggregating EPs in t measures the weight we put on each class; direct comparison of scores decides t's class. However the skewed distribution of EPs among classes and intricate relationship between EPs sometimes make the decision by directly comparing scores unreliable. In this paper, we propose to build Score Behaviour Knowledge Space (SBKS) to record the behaviour of training data on scores; classification decision is drawn from SBKS from a statistical point of view. Extensive experiments on real-world datasets show that SBKS frequently improves CAEP classifiers, especially on datasets where they have relatively poor performance. The improved CAEP classifiers outperform the start-of-the-art decision tree classifier C5.0.
47233627
Integrative negotiation in complex organizational agent systems
This paper introduces an integrative negotiation mechanism, which enables agents to choose any attitude from the extremes of self-interested and fully cooperative to those that are partially self-interested and partially cooperative. Experimental work verifies this mechanism and explores the question whether it always improves the social welfare to have an agent be completely cooperative. It is found that it is good for the organization to have agents being partially cooperative in their local negotiation with other agents rather than being fully cooperative, in order to deal more effectively with the uncertainty of not having a more informed view of the state of the entire agent organization.
8353152
An Efficient Algorithm for Mining Coherent Patterns from Heterogeneous Microarrays
DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.
12967582
An Improved Integer Local Search for Complex Scheduling Problems
We consider complex scheduling problems that can be captured as optimization under hard and soft constraints. The objective of such an optimization problem is to satisfy as many hard constraints as possible and meanwhile to minimize a penalty function determined by the unsatisfied soft constraints. We present an efficient local search algorithm for these problems which improves upon Wsat(oip), a Walksat-based local search algorithm for overconstrained problems represented in integer programs. We introduce three techniques to the Wsat(oip) algorithm to extend its capability and improve its performance: backbone guided biased moves to drive the search to the regions in search space where high-quality and optimal solutions reside; sampling-based aspiration search to reduce search cost and make anytime solutions available over the course of the search; and dynamic parameter tuning to dynamically adjust the key parameters of the algorithm to make it robust and flexible for various applications. Our experimental results on large-scale crew scheduling, basketball tournament scheduling and progressive party scheduling show that the new improved algorithm can find better solutions with less computation than Wsat(oip).
37914154
Collaborative relevance judgment: A group consensus method for evaluating user search performance
Relevance judgment has traditionally been considered a personal and subjective matter. A user's search and the search result are treated as an isolated event. To consider the collaborative nature of information retrieval (IR) in a group/organization or even societal context, this article proposes a method that measures relevance based on group/peer consensus. The method can be used in IR experiments. In this method, the relevance of a document is decided by group consensus, or more specifically, by the number of users (or experiment participants) who retrieve it for the same search question. The more users who retrieve it, the more relevant the document will be considered. A user's search performance can be measured by a relevance score based on this notion. The article reports the results of an experiment using this method to compare the search performance of different types of users. Related issues with the method and future directions are also discussed.
736327
A usage-based authorization framework for collaborative computing systems
Collaborative systems such as Grids provide efficient and scalable access to distributed computing capabilities and enable seamless resource sharing between users and platforms. This heterogeneous distribution of resources and the various modes of collaborations that exist between users, virtual organizations, and resource providers require scalable, flexible, and fine-grained access control to pro-tect both individual and shared computing resources. In this paper we propose a usage control (UCON) based authorization frame-work for collaborative applications. In our framework, usage con-trol policies are defined using subject and object attributes, along with system attributes as conditions. General attributes include not only persistent attributes such as role and group memberships, but also mutable usage attributes of subjects and objects. Conditions in UCON can be used to support context-based authorizations in ad-hoc collaborations. As a proof-of-concept we implement a pro-totype system based on our proposed architecture and conduct ex-perimental studies to demonstrate the feasibility and performance of our approach.