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
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.
5120213
Interaction between path and type constraints
XML [7], which is emerging as an important standard for data exchange on the World Wide Web, highlights the importance of the semistructured data. Although the XML standard itself does not require any schema or type system, a number of proposals [6, 15, 18] have been developed that roughly correspond to data definition languages. These allow one to constrain the structure of XML data by imposing a schema on it. These and other proposals also advocate the need for integrity constraints, another form of constraints that should, for example, be capable of expressing inclusion constraints and inverse relationships. The latter have recently been studied as path constraints in the context of semistructured data [4, 11]. It is likely that future XML proposals will involve both forms of constraint, and it is therefore appropriate to understand the interaction between them. This paper investigates that interaction. In particular it studies constraint implication problems, which are important both in understanding the semantics of type/constraint systems and in query optimization. A number of results on path constraint implication are established in the presences and absences of type systems. These results demonstrate that adding a type systems may in some cases simplify reasoning about path constraints and in other cases make it harder. For example, it is shown that there is a path constraint implication problem that is decidable in PTIME in the untyped context, but that becomes undecidable when a type system is added. On the other hand, there is an implication problem that is undecidable in the untyped context, but becomes not only decidable in cubic time but also finitely axiomatizable when a type system is imposed. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-98-16. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/106
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.
52830913
Interaction between path and type constraints
Path constraints are capable of expressing inclusion and inverse relationships and have proved useful in modeling and querying semistructured data [Abiteboul and Vianu 1999; Buneman et al. 2000]. Types also constrain the structure of data and are commonly found in traditional databases. There has also been work on imposing structure or a type system on semistructured data for storing and querying semistructured data in a traditional database system [Alon et al. 2001; Deutsch et al. 1999a; Florescu and Kossmann 1999; Shanmugasundaram et al. 1999]. One wants to know whether complexity results for reasoning about path constraints established in the untyped (semistructured) context could carry over to traditional databases, and vice versa. It is therefore appropriate to understand the interaction between types and path constraints. In addition, XML [Bray et al. 1998], which may involve both an optional schema (e.g., DTDs or XML Schema [Thompson et al. 2001]) and integrity constraints, highlights the importance of the study of the interaction.This article investigates that interaction. In particular it studies constraint implication problems, which are important both in understanding the semantics of type/constraint systems and in query optimization. It shows that path constraints interact with types in a highly intricate way. For that purpose a number of results on path constraint implication are established in the presence and absence of type systems. These results demonstrate that adding a type system may in some cases simplify reasoning about path constraints and in other cases make it harder. For example, it is shown that there is a path constraint implication problem that is decidable in PTIME in the untyped context, but that becomes undecidable when a type system is added. On the other hand, there is an implication problem that is undecidable in the untyped context, but becomes not only decidable in cubic time but also finitely axiomatizable when a type system is imposed.
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.
17406239
A generic ranking function discovery framework by genetic programming for information retrieval
Ranking functions play a substantial role in the performance of information retrieval (IR) systems and search engines. Although there are many ranking functions available in the IR literature, various empirical evaluation studies show that ranking functions do not perform consistently well across different contexts (queries, collections, users). Moreover, it is often difficult and very expensive for human beings to design optimal ranking functions that work well in all these contexts. In this paper, we propose a novel ranking function discovery framework based on Genetic Programming and show through various experiments how this new framework helps automate the ranking function design/discovery process.
9624007
Mining the web for answers to natural language questions
The web is now becoming one of the largest information and knowledge repositories. Many large scale search engines (Google, Fast, Northern Light, etc.) have emerged to help users find information. In this paper, we study how we can effectively use these existing search engines to mine the Web and discover the "correct" answers to factual natural language questions.We propose a probabilistic algorithm called QASM (Question Answering using Statistical Models) that learns the best query paraphrase of a natural language question. We validate our approach for both local and web search engines using questions from the TREC evaluation. We also show how this algorithm can be combined with another algorithm (AnSel) to produce precise answers to natural language questions.
1990595
IMMC: incremental maximum margin criterion
Subspace learning approaches have attracted much attention in academia recently. However, the classical batch algorithms no longer satisfy the applications on streaming data or large-scale data. To meet this desirability, Incremental Principal Component Analysis (IPCA) algorithm has been well established, but it is an unsupervised subspace learning approach and is not optimal for general classification tasks, such as face recognition and Web document categorization. In this paper, we propose an incremental supervised subspace learning algorithm, called Incremental Maximum Margin Criterion (IMMC), to infer an adaptive subspace by optimizing the Maximum Margin Criterion. We also present the proof for convergence of the proposed algorithm. Experimental results on both synthetic dataset and real world datasets show that IMMC converges to the similar subspace as that of batch approach.
16272457
SimFusion: measuring similarity using unified relationship matrix
In this paper we use a Unified Relationship Matrix (URM) to represent a set of heterogeneous data objects (e.g., web pages, queries) and their interrelationships (e.g., hyperlinks, user click-through sequences). We claim that iterative computations over the URM can help overcome the data sparseness problem and detect latent relationships among heterogeneous data objects, thus, can improve the quality of information applications that require com- bination of information from heterogeneous sources. To support our claim, we present a unified similarity-calculating algorithm, SimFusion. By iteratively computing over the URM, SimFusion can effectively integrate relationships from heterogeneous sources when measuring the similarity of two data objects. Experiments based on a web search engine query log and a web page collection demonstrate that SimFusion can improve similarity measurement of web objects over both traditional content based algorithms and the cutting edge SimRank algorithm.
9534472
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or generative language modeling, which requires explicit assumptions about the term distributions. The nowadays popular discriminative (classification, machine learning, statistical forecasting etc.) approaches have been mostly abandoned while solving this task in spite of their success in a different task of text categorization. In this paper, we studied if a classifier can be trained solely based on labeled examples to successfully generalize to new (unseen by the system) queries and provide performance comparable with popular heuristic or language models. Our SVM-based classifier learns from the relevance judgments available with the standard test collections and generalizes to new, previously unseen queries its ability to compare and rank documents with respect to a given query. To accomplish this, we have designed a representation scheme, which is based on the discretized form of the high level statistics of the query term occurrences (such as tf, df, and document length) rather than individual terms. Using the standard metrics of average precision and the standard large and small test collections we confirmed that our machine learning approach can achieve the performance comparable with and better than the performance of the current state of the art models.