12956058
Identifying meaningful return information for XML keyword search
Keyword search enables web users to easily access XML data without the need to learn a structured query language and to study possibly complex data schemas. Existing work has addressed the problem of selecting qualified data nodes that match keywords and connecting them in a meaningful way, in the spirit of inferring a where clause in XQuery. However, how to infer the return clause for keyword search is an open problem.
 To address this challenge, we present an XML keyword search engine, XSeek, to infer the semantics of the search and identify return nodes effectively. XSeek recognizes possible entities and attributes inherently represented in the data. It also distinguishes between search predicates and return specifications in the keywords. Then based on the analysis of both XML data structures and keyword match patterns, XSeek generates return nodes. Extensive experimental studies show the effectiveness of XSeek.
2511748
QPIAD: Query Processing over Incomplete Autonomous Databases
Incompleteness due to missing attribute values (aka "null values") is very common in autonomous Web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such relevant uncertain answers and gauge their relevance by accessing their likelihood of being relevant answers to the query. However, the autonomous nature of the databases poses several challenges, such as the restricted access privileges, limited query patterns, and sensitivity of database and network resource consumption in the Web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges to retrieve relevant uncertain answers. Our technique involves reformulating the user query based on approximate functional dependencies (AFDs) among the database attributes and ranking these queries using value distributions learned from naive Bayes classifiers. Empirical studies demonstrate the effectiveness of our approach in retrieving relevant uncertain answers with high precision, high recall and manageable cost.
37282807
Testing Web Services Using Progressive Group Testing
This paper proposes progressive group testing techniques to test large number of Web services (WS) available on Internet. At the unit testing level, the WS with the same functionality are tested in group using progressively increasing number of test cases. A small number of WS that scored best will be integrated into the real environment for operational testing. At the integration testing level, many composite services will be constructed and tested by group integration testing. The results of group testing at both unit and integration levels are verified by weighted majority voting mechanisms. The weights are based on the reliability history of the WS under test. A case study is designed and implemented, where the dependency among the test cases in WS is analyzed and used to generate progressive layers of test cases.
5453537
Testability of Software in Service-Oriented Architecture
Service-oriented architecture (SOA) is a system architecture in which a collection of loosely coupled services communicate with each other using standard interfaces and message-exchanging protocols. As an emerging technology in software development, the SOA presents a new paradigm, and it affects the entire software development cycle including analysis, specification, design, implementation, verification, validation, maintenance and evaluation. This paper proposes several testability evaluation criteria for SOA software, which serves as a reference for both service providers and application builders to evaluate the test support to SOA software. The proposed evaluation criteria are illustrated in a stock-trading case study
3132727
Query Processing over Incomplete Autonomous Databases
Incompleteness due to missing attribute values (aka "null values") is very common in autonomous web databases, on which user accesses are usually supported through mediators. Traditional query processing techniques that focus on the strict soundness of answer tuples often ignore tuples with critical missing attributes, even if they wind up being relevant to a user query. Ideally we would like the mediator to retrieve such possible answers and gauge their relevance by accessing their likelihood of being pertinent answers to the query. The autonomous nature of web databases poses several challenges in realizing this objective. Such challenges include the restricted access privileges imposed on the data, the limited support for query patterns, and the bounded pool of database and network resources in the web environment. We introduce a novel query rewriting and optimization framework QPIAD that tackles these challenges. Our technique involves reformulating the user query based on mined correlations among the database attributes. The reformulated queries are aimed at retrieving the relevant possible answers in addition to the certain answers. QPIAD is able to gauge the relevance of such queries allowing tradeoffs in reducing the costs of database query processing and answer transmission. To support this framework, we develop methods for mining attribute correlations (in terms of Approximate Functional Dependencies), value distributions (in the form of Naive Bayes Classifiers), and selectivity estimates. We present empirical studies to demonstrate that our approach is able to effectively retrieve relevant possible answers with high precision, high recall, and manageable cost.
5650965
L-Tree: A Dynamic Labeling Structure for Ordered XML Data
With the ever growing use of XML as a data representation format, we see an increasing need for robust, high performance XML database systems While most of the recent work focuses on efficient XML query processing, XML databases also need to support efficient updates To speed up query processing, various labeling schemes have been proposed However, the vast majority of these schemes have poor update performance In this paper, we introduce a dynamic labeling structure for XML data: L-Tree and its order-preserving labeling scheme with O(log n) amortized update cost and O(log n) bits per label L-Tree has good performance on updates without compromising the performance of query processing We present the update algorithm for L-Tree and analyze its complexity.
14708618
Enhancing UML Conceptual Modeling through the Use of Virtual Reality
UML is one of the major conceptual modeling tools in Object-Oriented software engineering. However, recently more and more concerns arise from the modeling quality of UML. Among them, limitation of the two-dimensional presentation of its notations is reported to be significant. By bringing in the concepts of Steoropsis (3-D appearance), animation, interaction and immersion, Virtual Reality may improve the UML modeling quality. Specifically, in this paper we analyze the modeling quality of UML by using recent published literature in conceptual modeling. We then recommend the use of Virtual Reality to enhance the modeling quality of UML. Some examples of how Virtual Reality can be used to improve UML modeling quality are given. Finally, we propose a research framework for enhancing the quality of UML conceptual modeling through the use of Virtual Reality.
16469592
IT businesses and franchising: a research proposal
Franchising has been popular as a growth strategy for small businesses; it is even more so in today's global and information-based economy. In early 2001, Entrepreneur magazine-well known for its Franchise 500 listing-added a tech businesses category to its franchise zone with three subcategories: Internet businesses, tech training, and miscellaneous tech businesses. This study applies Herbert Simon's model for business firm growth as a theory-grounded solution to help managers understand the growth dynamics of IT-based franchising businesses. We present a research proposal with a pilot study of the growth of 27 IT-based franchise businesses, and explain how to relate the parameters of Simon's model to strategic variables that can give managers a valuable strategic tool to understand the context of their industrial competition in IT-based franchising.
1548803
I/O-efficient techniques for computing pagerank
Over the last few years, most major search engines have integrated link-based ranking techniques in order to provide more accurate search results. One widely known approach is the Pagerank technique, which forms the basis of the Google ranking scheme, and which assigns a global importance measure to each page based on the importance of other pages pointing to it. The main advantage of the Pagerank measure is that it is independent of the query posed by a user; this means that it can be precomputed and then used to optimize the layout of the inverted index structure accordingly. However, computing the Pagerank measure requires implementing an iterative process on a massive graph corresponding to billions of web pages and hyperlinks.In this paper, we study I/O-efficient techniques to perform this iterative computation. We derive two algorithms for Pagerank based on techniques proposed for out-of-core graph algorithms, and compare them to two existing algorithms proposed by Haveliwala. We also consider the implementation of a recently proposed topic-sensitive version of Pagerank. Our experimental results show that for very large data sets, significant improvements over previous results can be achieved on machines with moderate amounts of memory. On the other hand, at most minor improvements are possible on data sets that are only moderately larger than memory, which is the case in many practical scenarios.
10406341
Concept Learning of Text Documents
Concept learning of text documents can be viewed as the problem of acquiring the definition of a general category of documents. To definite the category of a text document, the Conjunctive of keywords is usually be used. These keywords should be fewer and comprehensible. A naïve method is enumerating all combinations of keywords to extract suitable ones. However, because of the enormous number of keyword combinations, it is impossible to extract the most relevant keywords to describe the categories of documents by enumerating all possible combinations of keywords. Many heuristic methods are proposed, such as GA-base, immune based algorithm. In this work, we introduce pruning power technique and propose a robust enumeration-based concept learning algorithm. Experimental results show that the rules produce by our approach has more comprehensible and simplicity than by other methods.
31504377
Mapping DTDs to object-oriented schemas
As a subset of SGML, XML (Extensible Markup Language) is becoming a dominant standard for representing data in the World Wide Web and therefore the efficient treatment of XML data is important for information transfer over a network. One way towards this goal is to integrate database technology into document management and bring the very nature of database systems into this area, such as query processing, efficient management of secondary storage, version and update control, etc. We propose a new method to map DTDs (Document Type Definition) into object-oriented schemas. In this way, any complicated DTD structure can be treated uniformly. That is, using the concepts of classification/generalization and aggregation of the object-oriented model, any complex nested and recursive structure in a DTD as well as multiple appearance of element types can be represented. These issues can not be addressed if we map a DTD into a relational schema.