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.
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
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.
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.
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
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.
123606281
Combining null space-based Gabor features for face recognition
We propose a face recognition strategy combining various discriminating Gabor features in multi-scales and multi-orientations. A bank of well-chosen Gabor filters is applied on the image to construct a group of feature vectors, and then the Null Space-based LDA (NLDA) is performed simultaneously on each orientation channel and the original image to give 5 component classifier outputs, which are then combined to increase the final recognition rate. Experimental results on the FERET database demonstrate the effectiveness and flexibility of our proposed method.
1876424
A Similarity Reinforcement Algorithm for Heterogeneous Web Pages
Many machine learning and data mining algorithms crucially rely on the similarity metrics. However, most early research works such as Vector Space Model or Latent Semantic Index only used single relationship to measure the similarity of data objects. In this paper, we first use an Intra- and Inter- Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships. Then, we propose a novel similarity-calculating algorithm over the Inter- and Intra- Type Relationship Matrix. It tries to integrate information from heterogeneous sources to serve their purposes by iteratively computing. This algorithm can help detect latent relationships among heterogeneous data objects. Our new algorithm is based on the intuition that the intra-relationship should affect the inter-relationship, and vice versa. Experimental results on the MSN logs dataset show that our algorithm outperforms the traditional Cosine similarity.
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.
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.