bluuebunny/crossref_metadata_2025 huggingface dataset:
A Systematic Approach to Flexible Specifications, Composition, and Restructuring of Workflow Activities
Authors: Liu Ling , Pu Carlton , Ruiz Duncan
Published: February 2011

We introduce the ActivityFlow specification language for flexible specification, composition, and coordination of workflow activities. The most interesting features of the ActivityFlow specification language include: (1) a collection of specification mechanisms, allowing workflow designers to use a uniform workflow specification interface to describe different types (i.e., ad-hoc, administrative, or production) of workflows involved in their organizational processes– this feature helps to increase the flexibility of workflow processes in accommodating various types of changes; (2) a set of activity modeling facilities, enabling workflow designers to describe the flow of work declaratively and incrementally, allowing to reason about correctness and security of complex workflow activities independently from their underlying implementation mechanisms; (3) an open architecture that supports user interaction as well as collaboration of workflow systems of different organizations, and a set of workflow activity restructuring operators to respond to dynamic changes of workflow activities. We end the paper with a series of simulation-based experiments that demonstrate the effectiveness of these restructuring operators and the implementation architecture of the ActivityFlow system.
Informational and Computational Equivalence in Comparing Information Modeling Methods
Authors: Siau Keng
Published: February 2011

Information modeling methods are key to the success of IS development projects. The problem in the software engineering field is not the lack of modeling methods, but the proliferation of modeling methods with little theoretical foundation and empirical evidence to support their usefulness. Evaluation techniques are, therefore, necessary to compare and contrast these modeling methods. In this paper, we suggest an evaluation approach based on the human information processing paradigm and the theory of equivalence of representations. This evaluation approach proposes that modeling methods can be evaluated and compared using two criteria: informational and computational equivalence.
An Exploration of a Set Entropy-Based Hybrid Splitting Methods for Decision Tree Induction
Authors: Osei-Bryson Kweku-Muata , Giles Kendall
Published: February 2011

Decision tree (DT) induction is among the more popular of the data mining techniques. An important component of DT induction algorithms is the splitting method, with the most commonly used method being based on the Conditional Entropy family. However, it is well known that there is no single splitting method that will give the best performance for all problem instances. In this paper, we develop and explore hybrid splitting methods from two entropy-based families: the Conditional Entropy family and another family that is based on the Class-Attribute Mutual Information (CAMI). We compare conventional splitting methods based on single measures with hybrid splitting methods based on multiple measures. The results suggest that the hybrid methods could be competitive in terms of classification accuracy and are thus worthy of future research.
Scalable QSF-Trees
Authors: Orlandic Ratko , Yu Byunggu
Published: February 2011

Many database applications require effective representation of regional objects in high-dimensional spaces. By applying an original query transformation, a recently proposed access method for regional data, called the simple QSF-tree (sQSF-tree), effectively attacks the limitations of traditional spatial access methods in spaces with many dimensions. Nevertheless, sQSF-trees are not immune to all problems associated with high data dimensionality. Based on the analysis of sQSF-trees, this paper presents a new variant of sQSF-trees, called the scalable QSF-tree (cQSF-tree), which relies on a heuristic optimization to reduce the number of false drops into pages that contain no object satisfying the query. By increasing the selectivity of search predicates, cQSF-trees improve the performance of multi-dimensional selections. Experimental evidence shows that cQSF-trees are more scalable than sQSF-trees to the growing data dimensionality. The performance improvements also increase with more skewed data distribution.
A Database Interface for Link Analysis
Authors: Smith Mathew N. , King Peter J.H.
Published: February 2011

Understanding data gathered so far in a criminal or terrorist threat investigation is of great importance, particularly in guiding the future course of the investigation. One established method of better understanding the data is to visualize it as a network, showing how objects of interest are connected. In this article, we briefly describe this form of visual data analysis, the products available to support it and why current database support is lacking; plus, we present an experimental database interface for exploring data in this manner.
Experimental Study of a Self-Tuning Algorithm for DBMS Buffer Pools
Authors: Martin Patrick , Powley Wendy , Zheng Min , Romanufa Keri
Published: February 2011

The tasks of configuring and tuning large database management systems (DBMSs) have always been both complex and time-consuming. They require knowledge of the characteristics of the system, the data, and the workload, and of the interrelationships between them. The increasing diversity of the data and the workloads handled by today’s systems is making manual tuning by database administrators almost impossible. Self-tuning DBMSs, which dynamically reallocate resources in response to changes in their workload in order to maintain predefined levels of performance, are one approach to handling the tuning problem. In this paper, we apply self-tuning technology to managing the buffer pools, which are a key resource in a DBMS. Tuning the size of the buffer pools to a workload is crucial to achieving good performance. We describe a Buffer Pool Tuning Wizard that can be used by database administrators to determine effective buffer pool sizes. The wizard is based on a self-tuning algorithm called the Dynamic Reconfiguration algorithm (DRF), which uses the principle of goal-oriented resource management. It is an iterative algorithm that uses greedy heuristics to find a reallocation that benefits a target transaction class. We define and motivate the cost estimate equations used in the algorithm. We present the results of a set of experiments to investigate the performance of the algorithm.
Evaluating the Dynamic Behavior of Database Applications
Authors: He Zhen , Darmont Jérôme
Published: February 2011

This paper explores the effect that changing access patterns has on the performance of database management systems. Changes in access patterns play an important role in determining the efficiency of key performance optimization techniques, such as dynamic clustering, prefetching, and buffer replacement. However, all existing benchmarks or evaluation frameworks produce static access patterns in which objects are always accessed in the same order repeatedly. Hence, we have proposed the Dynamic Evaluation Framework (DEF) that simulates access pattern changes using configurable styles of change. DEF has been designed to be open and fully extensible (e.g., new access pattern change models can be added easily). In this paper, we instantiate DEF into the Dynamic Object Evaluation Framework (DoEF) which is designed for object databases, that is, object-oriented or object-relational databases, such as multimedia databases or most XML databases. The capabilities of DoEF have been evaluated by simulating the execution of four different dynamic clustering algorithms. The results confirm our analysis that flexible conservative reclustering is the key in determining a clustering algorithm’s ability to adapt to changes in access pattern. These results show the effectiveness of DoEF at determining the adaptability of each dynamic clustering algorithm to changes in access pattern in a simulation environment. In a second set of experiments, we have used DoEF to compare the performance of two real-life object stores: Platypus and SHORE. DoEF has helped to reveal the poor swapping performance of Platypus.
A Conceptual Design Methodology for Fuzzy Relational Databases
Authors: Ma Z.M.
Published: February 2011

Computer applications in the nontraditional area have put requirements on conceptual data modeling. Some conceptual data models, being the tools of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and design of databases. In this paper, based on fuzzy set and possibility distribution theory, different levels of fuzziness will be introduced into the IFO data model, and the corresponding graphical representations are given. IFO data model is then extended to the fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.
Conceptual Data Modeling Patterns
Authors: Batra Dinesh
Published: February 2011

The tremendous demand for software productivity has led to the idea of reuse of solutions that have worked successfully in the past. The notion of a design pattern is now well accepted in software design, and research in the area of data modeling has also begun. Although two books have explicitly attempted to cover this area, the representations provided in the books seem to be focused on specific applications and do not provide a generic and comprehensive set of templates. Another book attempts to address the problem but provides patterns at a level of granularity too small to be useful. This paper teases out underlying structures that tend to occur frequently in these books and provides patterns at an abstract and more useful level of granularity. It describes 11 data modeling patterns commonly found in business scenarios. The patterns are then validated by checking the frequency of occurrence of each pattern in the data representations included in three comprehensive texts of reference models. Two of these sources are targeted mainly at practitioners, and the third is academic oriented and targeted at students learning data modeling. Results indicate that although certain patterns are used more frequently than others, most of the 11 structures occur with adequate frequency to qualify as patterns. A comparison reveals that the frequency distribution of patterns is different among these sources. Further, the academic-oriented source distinctly focuses on different patterns as compared to the other two sources. The paper discusses the differences and provides specific recommendations on improving pedagogy in conceptual data modeling.
Enhancing Database Access Control by Facilitating Non-Key Related Cover Stories
Authors: Jukic Nenad , Nestorov Svetlozar , Vrbsky Susan V. , Parrish Allen
Published: February 2011

This paper presents an extension to a Multi-Level Secure (MLS) data model that requires the classification of data and users into multiple security levels. In MLS systems, cover stories allow information provided to users at lower security levels to differ from information provided to users at higher security levels. Previous versions of the MLS model did not permit cover stories for key attributes, because the key is used to relate the various cover stories for a particular entity. We have extended the MLS model to include non-key related cover stories so that key attributes can also have different values at different security levels. In this paper we describe the necessary model changes and modifications to the relational algebra, which are required to implement non-key related cover stories. We demonstrate the improvements made by these changes and discuss the implementation and performance of a system based on the described concepts.
Theoretical vs. Practical Complexity
Authors: Siau Keng , Erickson John , Lee LihYunn
Published: February 2011

Unified Modeling Language (UML) is the standard visual modeling language for Object Oriented (OO) systems development, but it has been criticized for its complexity, inconsistent semantics, and ambiguous constructs. A set of complexity indices for UML and the nine diagramming techniques in UML was compiled recently. The complexity analysis is formulated based on the number of constructs, associations, roles, and so forth, in a modeling method. We argue that this set of metrics provides an indication of the theoretical complexity of the modeling methods. On the other hand, the theoretical complexity of the modeling methods does not necessarily relate to the practical complexity. We hypothesize that UML’s complexity is not as daunting as the metrics imply, because not all the constructs are used all the time. Thus, in addition to theoretical complexity, a set of metrics for estimating practical complexity can be developed, based on the most commonly used constructs (instead of all constructs). In this research, we use secondary data to test our hypothesis that practical complexity is different from theoretical complexity.
Extending the OBJECTIVE Benchmark for Evaluation of Active Rules in a Distributed Component Integration Environment
Authors: Jin Ying , Urban Susan D. , Dietrich Suzanne W.
Published: February 2011

This research presents a benchmark for the evaluation of active rules within a distributed component integration environment. The benchmark is based on the OBJECTIVE active database benchmark. Our work was specifically performed in the context of the Integration Rules (IRules) project that uses active rules for the integration of distributed software components, which are based on the Enterprise JavaBeans component model. In addition to mapping the test rules of the OBJECTIVE benchmark to the integration rules of the IRules system, we have extended the OBJECTIVE benchmark to test additional features of a component-based active rule system, such as the event detection time, the rule execution time related to the invocation of methods on components, asynchronous coupling modes, and the rule processor execution time under a heavy event load. Although the research presents specific performance evaluation results for the IRules project, the approach more generally demonstrates the manner in which an existing rule processing benchmark for a centralized environment can be adapted and extended for use in a rule-based approach to distributed component integration.
Semantic Integration and Knowledge Discovery for Environmental Research
Authors: Chen Zhiyuan , Gangopadhyay Aryya , Karabatis George , McGuire Michael , Welty Claire
Published: February 2011

Environmental research and knowledge discovery both require extensive use of data stored in various sources and created in different ways for diverse purposes. We describe a new metadata approach to elicit semantic information from environmental data and implement semantic-based techniques to assist users in integrating, navigating, and mining multiple environmental data sources. Our system contains specifications of various environmental data sources and the relationships that are formed among them. User requests are augmented with semantically related data sources and automatically presented as a visual semantic network. In addition, we present a methodology for data navigation and pattern discovery using multi-resolution browsing and data mining. The data semantics are captured and utilized in terms of their patterns and trends at multiple levels of resolution. We present the efficacy of our methodology through experimental results.
View Materialization in a Data Cube
Authors: Agrawal Vikas , Sundararaghavan P. S. , Ahmed Mesbah U. , Nandkeolyar Udayan
Published: February 2011

Data warehouse has become an integral part in developing a DSS in any organization. One of the key architectural issues concerning the efficient design of a data warehouse is to determine the “right” number of views to be materialized in order to minimize the query response time experienced by the decision makers in the organization. We consider a bottleneck objective in designing such a materialization scheme which has the effect of guaranteeing a certain level of performance. We examine linear integer programming formulations, and develop heuristics and report on the performance of these heuristics. We also evaluate heuristics reported in the literature for the view materialization problem with a simpler objective.
Intelligent Search for Experts Using Fuzzy Abstraction Hierarchy in Knowledge Management Systems
Authors: Yang Kun-Woo , Huh Soon-Young
Published: February 2011

In knowledge management systems (KMS), managing explicit knowledge is comparatively easy using in-formation technology such as databases. However, tacit knowledge, usually possessed by human experts in unstructured forms such as know-how and experiences, is hard to systemize. Recent research has shown that it is more effective to provide search mechanisms for experts than to directly search for specific knowledge itself in KMS to pinpoint experts with needed knowledge in the organizations so that users can acquire the knowledge from the found experts. In this article, we propose an intelligent search framework to provide search capabilities for experts who not only match search conditions exactly but also belong to the similar or related subject fields according to the user’s needs. In enabling intelligent searches for experts, the Fuzzy Abstraction Hierarchy (FAH) framework has been adopted. Based on FAH, searching for experts with similar or related expertise is facilitated using the subject field hierarchy defined in the system. While adopting FAH, a text categorization approach based on Vector Space Model is also utilized to overcome the limitation of the original FAH framework. To test applicability and practicality of the proposed framework, the prototype system, “Knowledge Portal for Researchers in Science and Technology” sponsored by the Ministry of Science and Technology (MOST) of Korea, has been developed.
Enhancing UML Models
Authors: Reinhartz-Berger Iris , Sturm Arnon
Published: February 2011

UML has been largely adopted as a standard modeling language. The emergence of UML from different modeling languages that refer to various system aspects causes a wide variety of completeness and correctness problems in UML models. Several methods have been proposed for dealing with correctness issues, mainly providing internal consistency rules but ignoring correctness and completeness with respect to the system requirements and the domain constraints. In this article, we propose addressing both completeness and correctness problems of UML models by adopting a domain analysis approach called application-based domain modeling (ADOM). We present experimental results from our study which checks the quality of application models when utilizing ADOM on UML. The results advocate that the availability of the domain model helps achieve more complete models without reducing the comprehension of these models.
Bug Fixing Practices within Free/Libre Open Source Software Development Teams
Authors: Crowston Kevin , Scozzi Barbar
Published: February 2011

Free/Libre open source software (FLOSS, e.g., Linux or Apache) is primarily developed by distributed teams. Developers contribute from around the world and coordinate their activity almost exclusively by means of email and bulletin boards, yet some how profit from the advantages and evade the challenges of distributed software development. In this article we investigate the structure and the coordination practices adopted by development teams during the bug-fixing process, which is considered one of main areas of FLOSS project success. In particular, based on a codification of the messages recorded in the bug tracking system of four projects, we identify the accomplished tasks, the adopted coordination mechanisms, and the role undertaken by both the FLOSS development team and the FLOSS community. We conclude with suggestions for further research.
Adoption, Improvement, and Disruption
Authors: Brydon Michael , Vining Aidan R.
Published: February 2011

This article develops a model of open source disruption in enterprise software markets. It addresses the question: Is free and open source software (FOSS) likely to disrupt markets for commercial enterprise software? The conventional wisdom is that open source provision works best for low-level system-oriented technologies, while large, complex enterprise business applications are best served by commercial software vendors. The authors challenge the conventional wisdom by developing a two-stage model of open source disruption in enterprise software markets that emphasizes a virtuous cycle of adoption and lead-user improvement of the software. The two stages are an initial incubation stage (the I-Stage) and a subsequent snowball stage (the S-Stage). Case studies of several FOSS projects demonstrate the model’s ex post predictive value. The authors then apply the model to SugarCRM, an emerging open source CRM application, to make ex ante predictions regarding its potential to disrupt commercial CRM incumbents.
Using Iconic Graphics in Entity-Relationship Diagrams
Authors: Masri Kamal , Parker Drew , Gemino Andrew
Published: February 2011

This study reports on an experiment examining the impact of iconic graphics on participants’ understanding of domains represented by entity relationship diagrams. Cognitive load theory and the cognitive theory of multimedia learning are used to hypothesize that iconic graphics reduce the cognitive load of model viewers, leading to more complete mental models and consequently improved understanding. Results, as measured by transfer (problem solving) tasks, confirm the main hypothesis. Additionally, iconic images were found to be less effective in improving domain understanding with English-as-a-second-language (ESL) participants. ESL results are shown to be consistent with predictions based on the cognitive load theory.
The Quality of Online Privacy Policies
Authors: Storey Veda C. , Kane Gerald C. , Stewart Schwaig Kathy
Published: February 2011

Privacy concerns and practices, especially those dealing with the acquisition and use of consumer personal information by corporations, are at the forefront of business and social issues associated with the information age. This research examines the privacy policies of large U.S. companies to assess the substance and quality of their stated information practices. Six factors are identified that indicate the extent to which a firm is dependent upon consumer personal information, and therefore more likely to develop high quality privacy statements. The study’s findings provide practical and theoretical implications for information privacy issues, particularly for consumers who need to determine whether or not to disclose their personal identifying information to firms. The results illustrate the complexity involved in managing personal private information.
Reverse Engineering from an XML Document into an Extended DTD Graph
Authors: Shiu Herbert , Fong Joseph
Published: February 2011

Extensible markup language (XML) has become a standard for persistent storage and data interchange via the Internet due to its openness, self-descriptiveness, and flexibility. This article proposes a systematic approach to reverse engineer arbitrary XML documents to their conceptual schema–extended DTD graphs?which is a DTD graph with data semantics. The proposed approach not only determines the structure of the XML document, but also derives candidate data semantics from the XML element instances by treating each XML element instance as a record in a table of a relational database. One application of the determined data semantics is to verify the linkages among elements. Implicit and explicit referential linkages are among XML elements modeled by the parent-children structure and ID/IDREF(S) respectively. As a result, an arbitrary XML document can be reverse engineered into its conceptual schema in an extended DTD graph format.
Efficient Filtering of Branch Queries for High-Performance XML Data Services
Authors: Choi Ryan H. , Wong Raymond K.
Published: February 2011

Efficient XML filtering has been the fundamental technique in recent Web service and XML publish/subscribe applications. In this article, we consider the problem of filtering a streaming XML data efficiently against a large number of branch XPath queries. To improve the performance of XML filtering, branch queries are grouped into similar queries, and the common paths between queries in the same group are identified. After performing structural matching of queries, queries are organized in a way that multiple queries can be evaluated simultaneously in the post-processing phase. In the post-processing phase, join operations are executed in a pipeline fashion, and intermediate join results are shared amongst the queries in the same group. As a result, the total number of join operations performed in the post-processing phase is significantly reduced. In addition, we also present how to efficiently return all matching elements for each matching branch query. Experiments show that our proposal is efficient and scalable compared to previous work.
Towards Structured Flexibility in Information Systems Development
Authors: Karlsson Fredrik , Ågerfalk Pär J.
Published: February 2011

Method configuration is a specific type of Method Engineering (ME) that takes an existing organization-wide Information Systems Development Method (ISDM) as its point of departure. Existing assembly-based ME approaches are not well suited to this task. As an alternative, this article suggests a metamethod approach to tailoring organization-wide ISDMs. We refer to this approach as the Method for Method Configuration (MMC). MMC takes into account the need to combine structure, which is one reason for choosing an organization-wide ISDM in the first place, with flexibility, which is essential for making the chosen ISDM fit actual projects. The metamethod is built using a three-layered reuse model comprising method components, configuration packages, and configuration templates. These concepts are combined efficiently to produce a situational method and thereby to facilitate the work of method engineers.
An MDA Approach and QVT Transformations for the Integrated Development of Goal-Oriented Data Warehouses and Data Marts
Authors: Pardillo Jesús , Mazón Jose-Norberto , Trujillo Juan
Published: February 2011

To customize a data warehouse, many organizations develop concrete data marts focused on a particular department or business process. However, the integrated development of these data marts is an open problem for many organizations due to the technical and organizational challenges involved during the design of these repositories as a complete solution. In this article, the authors present a design approach that employs user requirements to build both corporate data warehouses and data marts in an integrated manner. The approach links information requirements to specific data marts elicited by using goal-oriented requirement engineering, which are automatically translated into the implementation of corresponding data repositories by means of model-driven engineering techniques. The authors provide two UML profiles that integrate the design of both data warehouses and data marts and a set of QVT transformations with which to automate this process. The advantage of this approach is that user requirements are captured from the early development stages of a data-warehousing project to automatically translate them into the entire data-warehousing platform, considering the different data marts. Finally, the authors provide screenshots of the CASE tools that support the approach, and a case study to show its benefits.
Developing a DSS for Allocating Gates to Flights at an International Airport
Authors: Yu Vincent F. , Murty Katta G. , Wan Yat-wah , Dann Jerry , Lee Robin
Published: February 2011

Typically international airports have features quite distinct from those of regional airports. We discuss the process of developing a Decision Support System, and appropriate mathematical models and algorithms to use for making gate allocation decisions at an international airport. As an example, we describe the application of this process at Taiwan Taoyuan International Airport to develop a DSS for making gate allocation decisions for their passenger flights.
A Systems Approach to the Determination of Control Strategy in a Hierarchical Manpower System
Authors: Škraba Andrej , Kljajic Miroljub , Kofjac Davorin
Published: February 2011

This article addresses the development and application of a simulation model of human resources transitions in the Slovenian armed forces. The system is modeled as an exponential delay chain by means of the Systems Dynamics methodology. The control strategy is determined according to the prescribed final states of a particular rank. In order to achieve the final states, the promotions policy between ranks and fluctuations could be changed by considering the boundaries of model parameters. The trajectory function is defined, which determines the approach to the final state. Numerical optimization was applied in order to provide a proper control strategy. The results show that some scenarios are not acceptable due to the problem of reachability. The developed system incorporates an advanced user interface that enables the efficient application of the developed methodology.
Examining the Implications of Process and Choice for
Authors: Drnevich Paul L. , Brush Thomas H. , Chaturvedi Alok
Published: February 2011

Most strategic decision-making (SDM) approaches advocate the importance of decision-making processes and response choices for obtaining effective outcomes. Modern decision-making support system (DMSS) technology is often also needed for complex SDM, with recent research calling for more integrative DMSS approaches. However, scholars tend to take disintegrated approaches and disagree on whether rational or political decision-making processes result in more effective decision outcomes. In this study, the authors examine these issues by first exploring some of the competing theoretical arguments for the process-choice-effectiveness relationship, and then test these relationships empirically using data from a crisis response training exercise using an intelligent agent-based DMSS. In contrast to prior research, findings indicate that rational decision processes are not effective in crisis contexts, and that political decision processes may negatively influence both response choice and decision effectiveness. These results offer empirical evidence to confirm prior unsupported arguments that response choice is an important mediating factor between the decision-making process and its effectiveness. The authors conclude with a discussion of the implications of these findings and the application of agent-based simulation DMSS technologies for academic research and practice.
Information Technology, Political Institutions, and Generalized Trust
Authors: Robbins Blaine , Grigoryeva Maria
Published: February 2011

In a recent study, the authors reveal with structural equation models that the positive effect of information technology on generalized trust is mediated by political institutions. Although insightful, a key question remains: Is it the effectiveness and efficiency, the universality, and/or the power-sharing capacity of the state that mediates this effect? Drawing on new institutional economics, political culture, and theories of the welfare state, the authors derive a number of hypotheses connecting information technology to generalized trust vis-à-vis elements of the state. The study shows with structural equation models that what accounts for the technology-trust relationship is not necessarily the public allocation of resources or political mechanisms of sharing power, but the incentive structures found in effective and efficient legal institutions that reduce uncertainty and increase generalized trust. The paper concludes by outlining the implications and directions for future research.
A Framework for Studying the Problem of Trust in Online Settings
Authors: Guenther Tina , Möllering Guido
Published: February 2011

The chapter contributes to the conceptual foundations of research on trust in online settings by introducing a framework of the preconditions and constitutive elements of trust. Moving beyond simplistic, narrow, and vague applications of the notion of trust, researchers are enabled by this framework to recognize when trust is relevant and to address a broader range of elements and processes involved in the social constitution of trust. By way of illustration and differentiation, the authors discuss trust issues in online marketplaces and online communities in greater detail. An important message from the chapter is that the problem of trust does not only occur in specific activities on a particular website but, more importantly, through the interconnectedness of the websites used and the development of complex online biographies. Accordingly, the authors advocate research methods that are not only are well-grounded conceptually but also geared praxeologically toward the actual experience and enactment of trust.
Periodic Streaming Data Reduction Using Flexible Adjustment of Time Section Size
Authors: Kim Jaehoon , Park Seog
Published: February 2011

Much of the research regarding streaming data has focused only on real time querying and analysis of recent data stream allowable in memory. However, as data stream mining, or tracking of past data streams, is often required, it becomes necessary to store large volumes of streaming data in stable storage. Moreover, as stable storage has restricted capacity, past data stream must be summarized. The summarization must be performed periodically because streaming data flows continuously, quickly, and endlessly. Therefore, in this paper, we propose an efficient periodic summarization method with a flexible storage allocation. It improves the overall estimation error by flexibly adjusting the size of the summarized data of each local time section. Additionally, as the processing overhead of compression and the disk I/O cost of decompression can be an important factor for quick summarization, we also consider setting the proper size of data stream to be summarized at a time. Some experimental results with artificial data sets as well as real life data show that our flexible approach is more efficient than the existing fixed approach.
Fuzzy Miner
Authors: Pelekis Nikos , Theodoulikis Babis , Kopanakis Ioannis , Theodoridis Yannis
Published: February 2011

We study the problem of classification as this is presented in the context of data mining. Among the various approaches that are investigated, we focus on the use of Fuzzy Logic for pattern classification, due to its close relation to human thinking. More specifically, this paper presents a heuristic fuzzy method for the classification of numerical data, followed by the design and the implementation of its corresponding tool (Fuzzy Miner). The initial idea comes from the fact that fuzzy systems are universal approximators of any real continuous function. Such an approximation method coming from the domain of fuzzy control is appropriately adjusted into pattern classification and an “adaptive” procedure is proposed for deriving highly accurate linguistic if-then rules. Extensive simulation tests are performed to demonstrate the performance of Fuzzy Miner, while a comparison with a neuro-fuzzy classifier of the area is taking place in order to contradict the methodologies and the corresponding outcomes. Finally, new research directions in the context of Fuzzy Miner are identified, and ideas for its improvement are formulated.
Novel Efficient Classifiers Based on Data Cube
Authors: Fu Lixin
Published: February 2011

Existing decision tree algorithms need to recursively partition dataset into subsets according to some splitting criteria. For large data sets, this requires multiple passes of original dataset and therefore is often infeasible in many applications. In this article we use statistics trees to compute the data cube and then build a decision tree on top of it. Mining on aggregated data will be much more efficient than directly mining on flat data files or relational databases. Since data cube server is usually a required component in an analytical system for answering OLAP queries, we essentially provide “free” classification. Our new algorithm generates trees of the same prediction accuracy as existing decision tree algorithms such as SPRINT and RainForest, but improves performance significantly. In this article we also give a system architecture that integrates DBMS, OLAP, and data mining seamlessly.
An Experimental Replication With Data Warehouse Metrics
Authors: Serrano Manuel , Calero Coral , Piattini Mario
Published: February 2011

Data warehouses are large repositories that integrate data from several sources for analysis and decision support. Data warehouse quality is crucial, because a bad data warehouse design may lead to the rejection of the decision support system or may result in non-productive decisions. In the last years, we have been working on the definition and validation of software metrics in order to assure data warehouse quality. Some of the metrics are adapted directly from previous ones defined for relational databases, and others are specific for data warehouses. In this paper, we present part of the empirical work we have developed in order to know if the proposed metrics can be used as indicators of data warehouse quality. Previously, we have developed an experiment and its replication, and in this paper, we present the second replication we have made with the purpose of assessing data warehouse maintainability. As a result of the whole empirical work, we have obtained a subset of the proposed metrics that seem to be good indicators of data warehouse quality.
Algebraic Reconstruction Technique in Image Reconstruction Based on Data Mining
Authors: Qu Zhong
Published: February 2011

Image reconstruction is one of the key technologies in industrial computed tomography. In this paper, an efficient iterative image reconstruction algorithm in industrial computed tomography with the narrow fan-beam projection based on data mining was discussed in detail. In image reconstruction, algebraic technique has un-replaceable advantage when data is incomplete or noise is high. However algebraic method has been highly limited in applications for its low reconstruction speed. In order to resolve this problem, the algebraic reconstruction technique (ART) as a new iterative method, is introduced to accelerate the iteration process and increase the reconstruction speed. Experiment results clearly demonstrate that the algorithm reconstruction technique can effectively improve the quality of images reconstruction in dealing with incomplete projection or noisy projection data.
Routing Attribute Data Mining Based on Rough Set Theory
Authors: Liu Yanbing , Sun Shixin , Wang Menghao , Tang Hong
Published: February 2011

QOSPF(Quality of Service Open Shortest Path First)based on QoS routing has been recognized as a missing piece in the evolution of QoS-based services in the Internet. Data mining has emerged as a tool for data analysis, discovery of new information, and autonomous decision-making. This paper focuses on routing algorithms and their applications for computing QoS routes in OSPF protocol. The proposed approach is based on a data mining approach using rough set theory, for which the attribute-value system about links of networks is created from network topology. Rough set theory of-fers a knowledge discovery approach to extracting routing-decisions from attribute set. The extracted rules can then be used to select significant routing-attributes and make routing-selections in routers. A case study is conducted to demonstrate that rough set theory is effective in finding the most significant attribute set. It is shown that the algo-rithm based on data mining and rough set offers a promising approach to the attribute-selection problem in internet routing.
Cluster-Based Input Selection for Transparent Fuzzy Modeling
Authors: Yang Can , Meng Jun , Zhu Shanan
Published: February 2011

Input selection is an important step in nonlinear regression modeling. By input selection, an interpretable model can be built with less computational cost. Input selection thus has drawn great attention in recent years. However, most available input selection methods are model-based. In this case, the input data selection is insensitive to changes. In this paper, an effective model-free method is proposed for the input selection. This method is based on sensitivity analysis using Minimum Cluster Volume (MCV) algorithm. The advantage of our proposed method is that with no specific model needed to be built in advance for checking possible input combinations, the computational cost is reduced and changes of data patterns can be captured automatically. The effectiveness of the proposed method is evaluated by using three well-known benchmark problems, which show that the proposed method works effectively with small and medium sized data collections. With an input selection procedure, a concise fuzzy model is constructed with high accuracy of prediction and better interpretation of data, which serves the purpose of patterns discovery in data mining well.
SeqPAM
Authors: Kumar Pradeep , Bapi Raju S. , Krishna P. Radha
Published: February 2011

With the growth in the number of Web users and necessity for making information available on the Web, the problem of Web personalization has become very critical and popular. Developers are trying to customize a Web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this chapter, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for Web personalization based on the clusters obtained from SeqPAM for msnbc dataset.
Classification Of 3G Mobile Phone Customers
Authors: Jain Ankur , Wangikar Lalit , Ahrens Martin , Rao Ranjan , Kundu Suddha Sattwa , Ghosh Sutirtha
Published: February 2011

In this article we discuss how we have predicted the third generation (3G) customers using lo-gistic regression analysis and statistical tools like Classification and Regression Tree (CART), Multivariate Adaptive Regression Splines (MARS), and other variables derived from the raw variables. The basic idea reflected in this paper is that the performance of logistic regression using raw variables standalone can be improved upon, by the use for various functions of the raw variables and dummies representing potential segments of the population.
Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting
Authors: Steinberg Dan , Golovnya Mikhaylo , Cardell Nicholas Scott
Published: February 2011

Mobile phone customers face many choices regarding handset hardware, add-on services, and features to subscribe to from their service providers. Mobile phone companies are now increas-ingly interested in the drivers of migration to third generation (3G) hardware and services. Using real world data provided to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition we explore the effectiveness of Friedman’s stochastic gradient boosting (Multiple Additive Regression Trees [MART]) for the rapid development of a high performance predictive model.
An Immune Systems Approach for Classifying Mobile Phone Usage
Authors: Limanto Hanny Yulius , Cing Tay Joc , Watkins Andrew
Published: February 2011

With the recent introduction of third generation (3G) technology in the field of mobile commu-nications, mobile phone service providers will have to find an effective strategy to market this new technology. One approach is to analyze the current profile of existing 3G subscribers to discover common patterns in their usage of mobile phones. With these usage patterns, the service provider can effectively target certain classes of customers who are more likely to purchase their subscription plans. To discover these patterns, we use a novel algorithm called Artificial Immune Recognition System (AIRS) that is based on the specificity of the human immune system. In our experiment, the algorithm performs well, achieving an accuracy rate in the range of 80% to 90%, depending on the set of parameter values used.
A Single Pass Algorithm for Discovering Significant Intervals in Time-Series Data
Authors: Savla Sagar , Chakravarthy Sharma
Published: February 2011

Sensor-based applications, such as smart homes, require prediction of event occurrences for automating the environment using time-series data collected over a period of time. In these applications, it is important to predict events in tight and accurate intervals to effectively automate the application. This article deals with the discovery of significant intervals from time-series data. Although there is a considerable body of work on sequential mining of transactional data, most of them deal with time-point data and make several passes over the entire data set in order to discover frequently occurring patterns/events. We propose an approach in which significant intervals representing intrinsic nature of data are discovered in a single pass. In our approach, time-series data is folded over a periodicity (day, week, etc.) in which the intervals are formed. Significant intervals are discovered from this interval data that satisfy the criteria of minimum confidence and maximum interval length specified by the user. Both compression and working with intervals contribute towards improving the efficiency of the algorithm. In this article, we present a new single-pass algorithm for detecting significant intervals; discuss its characteristics, advantages, and disadvantages; and analyze it. Finally, we compare the performance of our algorithm with previously developed level-wise and SQL-based algorithms for significant interval discovery (SID).
Managing Late Measurements in Data Warehouses
Authors: Golfarelli Matteo , Rizzi Stefano
Published: February 2011

Though in most data warehousing applications no relevance is given to the time when events are recorded, some domains call for a different behavior. In particular, whenever late measurements of events take place, and particularly when the events registered are subject to further updates, the traditional design solutions fail in preserving accountability and query consistency. In this article, we discuss the alternative design solutions that can be adopted, in presence of late measurements, to support different types of queries that enable meaningful historical analysis. These solutions are based on the enforcement of the distinction between transaction time and valid time within the schema that represents the fact of interest. Besides, we provide a qualitative and quantitative comparison of the solutions proposed, aimed at enabling wellinformed design decisions.