18510844
Structural Properties of XPath Fragments
We study structural properties of each of the main sublanguages of XPath [8] commonly used in practice. First, we characterize the expressive power of these language fragments in terms of both logics and tree patterns. Second, we investigate closure properties, focusing on the ability to perform basic Boolean operations while remaining within the fragment. We give a complete picture of the closure properties of these fragments, treating XPath expressions both as functions of arbitrary nodes in a document tree, and as functions that are applied only at the root of the tree. Finally, we provide sound and complete axiom systems and normal forms for several of these fragments. These results are useful for simplification of XPath expressions and optimization of XML queries.
8618813
Reasoning about Keys for XML
We study absolute and relative keys for XML, and investigate their associated decision problems. We argue that these keys are important to many forms of hierarchically structured data including XML documents. In contrast to other proposals of keys for XML, these keys can be reasoned about efficiently. We show that the (finite) satisfiability problem for these keys is trivial, and their (finite) implication problem is finitely axiomatizable and decidable in PTIME in the size of keys.
52151587
On XML integrity constraints in the presence of DTDs
The article investigates XML document specifications with DTDs and integrity constraints, such as keys and foreign keys. We study the consistency problem of checking whether a given specification is meaningful: that is, whether there exists an XML document that both conforms to the DTD and satisfies the constraints. We show that DTDs interact with constraints in a highly intricate way and as a result, the consistency problem in general is undecidable. When it comes to unary keys and foreign keys, the consistency problem is shown to be NP-complete. This is done by coding DTDs and integrity constraints with linear constraints on the integers. We consider the variations of the problem (by both restricting and enlarging the class of constraints), and identify a number of tractable cases, as well as a number of additional NP-complete ones. By incorporating negations of constraints, we establish complexity bounds on the implication problem, which is shown to be coNP-complete for unary keys and foreign keys.
553416
Path Constraints in Semistructured Databases
We investigate a class of path constraints that is of interest in connection with both semistructured and structured data. In standard database systems, constraints are typically expressed as part of the schema, but in semistructured data there is no explicit schema and path constraints provide a natural alternative. As with structured data, path constraints on semistructured data express integrity constraints associated with the semantics of data and are important in query optimization. We show that in semistructured databases, despite the simple syntax of the constraints, their associated implication problem is r.e. complete and finite implication problem is co-r.e. complete. However, we establish the decidability of the implication and finite implication problems for several fragments of the path constraint language and demonstrate that these fragments suffice to express important semantic information such as extent constraints, inverse relationships, and local database constraints commonly found in object-oriented databases.
38519945
Propagating XML constraints to relations
We present a technique for refining the design of relational storage for XML data based on XML key propagation. Three algorithms are presented: one checks whether a given functional dependency is propagated from XML keys via a predefined view; the others compute a minimum cover for all functional dependencies on a universal relation given XML keys. Experimental results show that these algorithms are efficient in practice. We also investigate the complexity of propagating other XML constraints to relations, and the effect of increasing the power of the transformation language. Computing XML key propagation is a first step toward establishing a connection between XML data and its relational representation at the semantic level.
10449065
Beyond XML Query Languages
A query language is essential, if XML is to serve effectively as an exchange medium for large data sets. The design of query languages for XML is in its infancy, and the choice of a standard may be governed more by user acceptance than by any understanding of underlying principles. One would hope that expressive power, performance, and compatibility with other languages will be considered in choosing among alternatives, but it is likely that several contenders will co-exist for some time. It is worth observing that, during the 20-year development of relational query languages, several competing languages were developed; and even today there are several relational query language standards. In spite of this, a great deal of technology was developed that was independent of the surface syntax of a query language. This included technology "below" the language such as efficient execution models and work "above" the level of language such as techniques for view definition and maintenance, triggers, etc. At Penn we are working on some of these language-independent issues. We include a summary of them here. They include execution and data models to support XML and semistructured query languages; the use of schemas and constraints in optimizing XML query languages; and tools for extracting data form existing sources and presenting it as XML. Comments Postprint version. Published in Query Language Workshop, November 1998. Publisher URL: http://www.w3.org/TandS/QL/QL98/pp/penn.html Author(s) Peter Buneman, Alin Deutsch, Wenfei Fan, Hartmut Liefke, Arnaud Sahuguet, and Wang-Chiew Tan This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/db_research/14 Beyond XML Query Languages Peter Buneman Alin Deutsch Wenfei Fan Hartmut Liefke Arnaud Sahuguet Wang-Chiew Tan University of Pennsylvania fpeter,adeutsch,wfan,liefke,sahuguet,wctang@saul.cis.upenn.edu
14798510
Personalization of search engine services for effective retrieval and knowledge management
The Internet and corporate intranets provide far more information than anybody can absorb. People use search engines to find the information they require. However, these systems tend to use only one fixed term weighting strategy regardless of the context to which it applies, posing serious performance problems when characteristics of different users, queries, and text collections are taken into consideration. In this paper, we argue that the term weighting strategy should be context specific, that is, different term weighting strategies should be applied to different contexts, and we propose a new systematic approach that can automatically generate term weighting strategies for different contexts based on genetic programming (GP). The new proposed framework was tested on TREC data and the results are very promising.
9423660
Learning to advertise
Content-targeted advertising, the task of automatically associating ads to a Web page, constitutes a key Web monetization strategy nowadays. Further, it introduces new challenging technical problems and raises interesting questions. For instance, how to design ranking functions able to satisfy conflicting goals such as selecting advertisements (ads) that are relevant to the users and suitable and profitable to the publishers and advertisers? In this paper we propose a new framework for associating ads with web pages based on Genetic Programming (GP). Our GP method aims at learning functions that select the most appropriate ads, given the contents of a Web page. These ranking functions are designed to optimize overall precision and minimize the number of misplacements. By using a real ad collection and web pages from a newspaper, we obtained a gain over a state-of-the-art baseline method of 61.7% in average precision. Further, by evolving individuals to provide good ranking estimations, GP was able to discover ranking functions that are very effective in placing ads in web pages while avoiding irrelevant ones.
5459818
Putting context into schema matching
Attribute-level schema matching has proven to be an important first step in developing mappings for data exchange, integration, restructuring and schema evolution. In this paper we investigate contextual schema matching, in which selection conditions are associated with matches by the schema matching process in order to improve overall match quality. We define a general space of matching techniques, and within this framework we identify a variety of novel, concrete algorithms for contextual schema matching. Furthermore, we show how common schema mapping techniques can be generalized to take more effective advantage of contextual matches, enabling automatic construction of mappings across certain forms of schema heterogeneity. An experimental study examines a wide variety of quality and performance issues. In addition, it demonstrates that contextual schema matching is an effective and practical technique to further automate the definition of complex data transformations.
16918412
Ranking function optimization for effective Web search by genetic programming: an empirical study
Web search engines have become indispensable in our daily life to help us find the information we need. Although search engines are very fast in search response time, their effectiveness in finding useful and relevant documents at the top of the search hit list needs to be improved. In this paper, we report our experience applying genetic programming (GP) to the ranking function discovery problem leveraging the structural information of HTML documents. Our empirical experiments using the Web track data from recent TREC conferences show that we can discover better ranking functions than existing well-known ranking strategies from IR, such as Okapi, Ptfidf. The performance is even comparable to those obtained by support vector machine.
7558405
Intelligent fusion of structural and citation-based evidence for text classification
This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.