Information source tracking method: efficiency issues

This paper is devoted to the study and analysis of query processing efficiency in the information source tracking (IST) technique, an approach to the representation and manipulation of uncertain and inaccurate data. We show that the efficiency depends on the average number of information sources confirming the same data in the database. If this number is close to unity, then the efficiency of query processing for the IST model is comparable to conventional relational database systems. For the case where multiple information sources confirm the same data in the database, we present a variation of IST, called the Dual IST method, which provides efficient query processing. Extended relational algebra operations are presented for Dual IST, and proven to be correct under the “alternate worlds” semantics interpretation. The complexity of reliability calculation in IST and Dual IST methods is also studied     

Incomplete deductive databases

We investigate the complexity of query processing in databases which have both incompletely specified data and deductive rules. The paper is divided into two parts: in the first we consider databases in which incompletely specified data occurs only in the database intension; in the second we consider databases in which incomplete information is represented only in database extension. We prove that, in general, the query processing problem for databases with incomplete intensions is undecidable. A number of classes of rules for which all conjunctive queries can be processed in polynomial time is then characterized. For databases with incomplete extensions we prove a number of CoNP completeness results. For instance, we demonstrate that processing disjunctions which are restricted to individual columns of database predicates can, in general, be as bad as processing arbitrary disjunctions (i.e. CoNP-complete). This falsifies the conjecture that such limited disjunctions could be computationally beneficial. We also show two simple examples of situations in which query processing is guaranteed to be polynomial. These situations are linked to certain assumptions about database updates.Finally, we provide a summary of the data complexity of queries depending on the type of database extension, intension, query sublanguage and Open World vs Closed World assumption.Research supported by NSF grant DCR 85-04140.More precisely, we can say this only in the presence of the closed world assumption [18].     

Approximation-Based Similarity Search for 3-D Surface Segments

The issue of finding similar 3-D surface segments arises in many recent applications of spatial database systems, such as molecular biology, medical imaging, CAD, and geographic information systems. Surface segments being similar in shape to a given query segment are to be retrieved from the database. The two main questions are how to define shape similarity and how to efficiently execute similarity search queries. We propose a new similarity model based on shape approximation by multi-parametric surface functions that are adaptable to specific application domains. We then define shape similarity of two 3-D surface segments in terms of their mutual approximation errors. Applying the multi-step query processing paradigm, we propose algorithms to efficiently support complex similarity search queries in large spatial databases. A new query type, called the ellipsoid query, is utilized in the filter step. Ellipsoid queries, being specified by quadratic forms, represent a general concept for similarity search. Our major contribution is the introduction of efficient algorithms to perform ellipsoid queries on multidimensional index structures. Experimental results on a large 3-D protein database containing 94,000 surface segments demonstrate the successful application and the high performance of our method.     

A data model and algebra for probabilistic complex values

We present a probabilistic data model for complex values. More precisely, we introduce probabilistic complex value relations, which combine the concept of probabilistic relations with the idea of complex values in a uniform framework. We elaborate a model-theoretic definition of probabilistic combination strategies, which has a rigorous foundation on probability theory. We then define an algebra for querying database instances, which comprises the operations of selection, projection, renaming, join, Cartesian product, union, intersection, and difference. We prove that our data model and algebra for probabilistic complex values generalizes the classical relational data model and algebra. Moreover, we show that under certain assumptions, all our algebraic operations are tractable. We finally show that most of the query equivalences of classical relational algebra carry over to our algebra on probabilistic complex value relations. Hence, query optimization techniques for classical relational algebra can easily be applied to optimize queries on probabilistic complex value relations.     

XML queries and algebra in the Enosys integration platform

We describe the Enosys XML integration platform, focusing on the query language, algebra, and architecture of its query processor. The platform enables the development of eBusiness applications in customer relationship management, e-commerce, supply chain management, and decision support. These applications often require that data be integrated dynamically from multiple information sources. The Enosys platform allows one to build (virtual and/or materialized) integrated XML views of multiple sources, using XML queries as view definitions. During run-time, the application issues XML queries against the views. Queries and views are translated into the XCQL algebra and are combined into a single algebra expression/plan. Query plan composition and query plan decomposition challenges are faced in this process. Finally, the query processor lazily evaluates the result, using an appropriate adaptation of relational database iterator models to XML. The paper describes the platform architecture and components, the supported XML query language and the query processor architecture. It focuses on the underlying XML query algebra, which differs from the algebras that have been considered by W3C in that it is particularly tuned to semistructured data and to optimization and efficient evaluation in a system that follows the conventional architecture of database systems.     

HyperFile: A data and query model for documents

Non-quantitative information such as documents and pictures pose interesting new problems in the database world. Traditional data models and query languages do not provide appropriate support for this information. Such data are typically stored in file systems, which do not provide the security, integrity, or query features of database management systems. The hypertext model has emerged as a good interface to this information; however,finding information using hypertext browsing does not scale well. We developed a query interface that serves as an extension of the browsing model of hypertext systems. These queries minimize the repeated user interactions required to locate data in a standard hypertext system. HyperFile is a prototype data server interface. In this article, we describe HyperFile, including a number of issues such as query generation, query processing, and indexing.     

Object-oriented query language access to relational databases: A semantic framework for query translation

This research investigates and approach to query processing in a multidatabase system that uses an objectoriented model to capture the semantics of other data models. The object-oriented model is used to construct a global schema, defining an integrated view of the different schemas in the environment. The model is also used as a self-describing model to build a meta-database for storing information about the global schema. A unique aspect of this work is that the object-oriented model is used to describe the different data models of the multidatabase environment, thereby extending the meta database with semantic information about the local schemas. With the global and local schemas all represented in an object-oriented form, structural mappings between the global schema and each local schema are then easily supported. An object algebra then provides a query language for expressing global queries, using the structural mappings to translate object algebra queries into SQL queries over local relational schema. The advantage of using an object algebra is that the object-oriented database can be viewed as a blackboard for temporary storage of local data and for establishing relationships between different databases. The object algebra can be used to directly retrieve temporarily-stored data from the object-oriented database or to transparently retrieve data from local sources using the translation process described in this paper.     

Fuzzy querying of incomplete, imprecise, and heterogeneously structured data in the relational model using ontologies and rules

In this paper, we present a new method, called multiview fuzzy querying, which permits to query incomplete, imprecise and heterogeneously structured data stored in a relational database. This method has been implemented in the MIEL software. MIEL is used to query the Sym'Previus database which gathers information about the behavior of pathogenic germs in food products. In this database, data are incomplete because information about all possible food products and all possible germs is not available; data are heterogeneous because they come from various sources (scientific bibliography, industrial data, etc); data may be imprecise because of the complexity of the underlying biological processes that are involved. To deal with heterogeneity, MIEL queries the database through several views simultaneously. To query incomplete data, MIEL proposes to use a fuzzy set, expressing the query preferences of the user. Fuzzy sets may be defined on a hierarchized domain of values, called an ontology (values of the domain are connected using the a kind of semantic link). MIEL also proposes two optional functionalities to help the user query the database: i) MIEL can use the ontology to enlarge the querying in order to retrieve the nearest data corresponding to the selection criteria; and ii) MIEL proposes fuzzy completion rules to help the user formulate his/her query. To query imprecise data stored in the database with fuzzy selection criteria, MIEL uses fuzzy pattern matching.     

多重空间映射与超结构查询

多重空间映射是将多个信息系统数据库中的事物组织成多层逻辑空间,通过映射快速地确定查询对象。超结构查询是通过“多重空间映射”的方法,实现超越应用系统结构的、快速、方便、广泛的信息查询的一种新技术。它不受功能系统的限制,不受数据库表结构的限制,能够在各个功能系统的多个数据库表中查找匹配的信息,可有效解决多个应用系统范围的数据查询,并使信息系统中的数据得到更充分的利用。     

基于语义近似逼近技术的多代理协作

本体在多代理系统中起着重要的作用,它提供和定义了一个共享的语义词汇库。然而,在现实的多代理通讯的过程中,两个代理共享完全相同的语义词汇库是几乎不可能的。因为信息不完整以及本体的异构等特性,一个代理只能部分理解另外一个代理所拥有的本体内容,这使得代理间的通讯非常困难。本文就是探索利用近似逼近技术实现基于部分共享分布式本体的多代理通讯,从而实现多代理之间的协作查询。我们使用基于OWLweb本体语言的描述逻辑来描述分布式本体的近似查询技术。最终我们也开发了基于语义近似逼近方法的一个多代理协调查询系统。     

Conversational querying

The traditional interaction mechanism with a database system is through the use of a query language, the most widely used one being SQL. However, when one is facing a situation where he or she has to make a minor modification to a previously issued SQL query, either the whole query has to be written from scratch, or one has to invoke an editor to edit the query. This, however, is not the way we converse with each other as humans. During the course of a conversation, the preceding interaction is used as a context within which many incomplete and/or incremental phrases are uniquely and unambiguously interpreted, sparing the need to repeat the same things again and again. In this paper, we present an effective mechanism that allows a user to interact with a database system in a way similar to the way humans converse. More specifically, incomplete SQL queries are accepted as input which are then matched to identified parts of previously issued queries. Disambiguation is achieved by using various types of semantic information. The overall method works independently of the domain under which it is used (i.e., independently of the database schema). Several algorithms that are variations of the same basic mechanism are proposed. They are mutually compared with respect to efficiency and accuracy through a limited set of experiments on human subjects. The results have been encouraging, especially when semantic knowledge from the schema is exploited, laying a potential foundation for conversational querying in databases.     

On the finite controllability of conjunctive query answering in databases under open-world assumption

In this paper we study queries over relational databases with integrity constraints (ICs). The main problem we analyze is OWA query answering, i.e., query answering over a database with ICs under open-world assumption. The kinds of ICs that we consider are inclusion dependencies and functional dependencies, in particular key dependencies; the query languages we consider are conjunctive queries and unions of conjunctive queries. We present results about the decidability of OWA query answering under ICs. In particular, we study OWA query answering both over finite databases and over unrestricted databases, and identify the cases in which such a problem is finitely controllable, i.e., when OWA query answering over finite databases coincides with OWA query answering over unrestricted databases. Moreover, we are able to easily turn the above results into new results about implication of ICs and query containment under ICs, due to the deep relationship between OWA query answering and these two classical problems in database theory. In particular, we close two long-standing open problems in query containment, since we prove finite controllability of containment of conjunctive queries both under arbitrary inclusion dependencies and under key and foreign key dependencies. The results of our investigation are very relevant in many research areas which have recently dealt with databases under an incomplete information assumption: e.g., data integration, data exchange, view-based information access, ontology-based information systems, and peer data management systems.     

Incomplete data management: a survey

Incomplete data accompanies our life processes and covers almost all fields of scientific studies, as a result of delivery failure, no power of battery, accidental loss, etc. However, how to model, index, and query incomplete data incurs big challenges. For example, the queries struggling with incomplete data usually have dissatisfying query results due to the improper incompleteness handling methods. In this paper, we systematically review the management of incomplete data, including modelling, indexing, querying, and handling methods in terms of incomplete data. We also overview several application scenarios of incomplete data, and summarize the existing systems related to incomplete data. It is our hope that this survey could provide insights to the database community on how incomplete data is managed, and inspire database researchers to develop more advanced processing techniques and tools to cope with the issues resulting from incomplete data in the real world.     

Taxonomy-based relaxation of query answering in relational databases

Traditional information search in which queries are posed against a known and rigid schema over a structured database is shifting toward a Web scenario in which exposed schemas are vague or absent and data come from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and a class of abstract query languages as a foundation for querying relational data sets with vague schemas. Our approach relies on the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization between terms. We first propose a conservative extension of the relational algebra for this model in which special operators allow the specification of relaxed queries over vaguely structured information. We also study equivalence and rewriting properties of the algebra that can be used for query optimization. We then illustrate a logic-based query language that can provide a basis for expressing relaxed queries in a declarative way. We finally investigate the expressive power of the proposed query languages and the independence of the taxonomy in this context.     

Querying through a user interface

In contrast to a traditional setting where users express queries against the database schema, we assert that the semantics of data can often be understood by viewing the data in the context of the user interface (UI) of the software tool used to enter the data. That is, we believe that users will understand the data in a database by seeing the labels, drop-down menus, tool tips, or other help text that are built into the user interface. Our goal is to allow domain experts with little technical skill to understand and query data. In this paper, we present our GUi As View (Guava) framework and describe how we use forms-based UIs to generate a conceptual model that represents the information in the user interface. We then describe how we generate a query interface from the conceptual model. We characterize the resulting query language using a subset of the relational algebra. Since most application developers want to craft a physical database to meet desired performance needs, we present here a transformation channel that can be configured by instantiating one or more of our transformation operators. The channel, once configured, automatically transforms queries from our query interface into queries that address the underlying physical database and delivers query results that conform to our query interface. In this paper, we define and formalize our database transformation operators. The contributions of this paper are that first, we demonstrate the feasibility of creating a query interface based directly on the user interface and second, we introduce a general purpose database transformation channel that will likely shorten the application development process and increase the quality of the software by automatically generating software artifacts that are often made manually and are prone to errors.     

Querying through a user interface

In contrast to a traditional setting where users express queries against the database schema, we assert that the semantics of data can often be understood by viewing the data in the context of the user interface (UI) of the software tool used to enter the data. That is, we believe that users will understand the data in a database by seeing the labels, drop-down menus, tool tips, or other help text that are built into the user interface. Our goal is to allow domain experts with little technical skill to understand and query data. In this paper, we present our GUi As View (Guava) framework and describe how we use forms-based UIs to generate a conceptual model that represents the information in the user interface. We then describe how we generate a query interface from the conceptual model. We characterize the resulting query language using a subset of the relational algebra. Since most application developers want to craft a physical database to meet desired performance needs, we present here a transformation channel that can be configured by instantiating one or more of our transformation operators. The channel, once configured, automatically transforms queries from our query interface into queries that address the underlying physical database and delivers query results that conform to our query interface. In this paper, we define and formalize our database transformation operators. The contributions of this paper are that first, we demonstrate the feasibility of creating a query interface based directly on the user interface and second, we introduce a general purpose database transformation channel that will likely shorten the application development process and increase the quality of the software by automatically generating software artifacts that are often made manually and are prone to errors.     

Enhancing accuracy and expressive power of range query answers over incomplete spatial databases via a novel reasoning approach

Modern spatial database applications built on top of distributed and heterogeneous spatial information sources such as conventional spatial databases underlying Geographical Information Systems (GIS), spatial data files and spatial information acquired or inferred from the Web, suffer from data integration and topological consistency problems. This more-and-more conveys in incomplete information, which makes answering range queries over incomplete spatial databases a leading research challenge in spatial database systems research. A significant instance of this setting is represented by the application scenario in which the geometrical information on a sub-set of spatial database objects is incomplete whereas the spatial database still stores topological relations among these objects (e.g., containment relations). Focusing on the spatial database application scenario above, in this paper we propose and experimentally assess a novel technique for efficiently answering range queries over incomplete spatial databases via integrating geometrical information and topological reasoning. We also propose I-SQE (Spatial Query Engine for Incomplete Information), an innovative query engine implementing this technique. Our proposed technique results not only effective but also efficient against both synthetic and real-life spatial data sets, and it finally allows us to enhance the quality and the expressive power of retrieved answers by meaningfully taking advantages from the amenity of representing spatial database objects via both the geometrical and the topological level.     

How to Comprehend Queries Functionally

Compilers and optimizers for declarative query languages use some form of intermediate language to represent user-level queries. The advent of compositional query languages for orthogonal type systems (e.g., OQL) calls for internal query representations beyond extensions of relational algebra. This work adopts a view of query processing which is greatly influenced by ideas from the functional programming domain. A uniform formal framework is presented which covers all query translation phases, including user-level query language compilation, query optimization, and execution plan generation. We pursue the type-based design—based on initial algebras—of a core functional language which is then developed into an intermediate representation that fits the needs of advanced query processing. Based on the principle of structural recursion we extend the language by monad comprehensions (which provide us with a calculus-style sublanguage that proves to be useful during the optimization of nested queries) and combinators (abstractions of the query operators implemented by the underlying target query engine). Due to its functional nature, the language is susceptible to program transformation techniques that were developed by the functional programming as well as the functional data model communities. We show how database query processing can substantially benefit from these techniques.     

Web信息的结构化及其查询视图研究

Web并不是传统的关系数据库。在WWW浏览器上进行信息搜索时,常常难于快速有效地得到查询结果。就Web站点信息库的数据模型化、关系代数演算及关系视图的建立加以探讨,基于这一模式结构的Web站点信息查询便易于表达和实现。     

Querying and ranking incomplete twigs in probabilistic XML

As the next generation language of the Internet, XML has been the de-facto standard of information exchange over the web. A core operation for XML query processing is to find all the occurrences of a twig pattern in an XML database. In addition, the study of probabilistic data has become an emerging topic for various applications on the Web. Therefore, researching the combination of XML twig pattern and probabilistic data is quite significant. In prior work of probabilistic XML, the answers of a given twig query are always complete. However, complete answers with low probabilities may be deemed irrelevant while incomplete answers with high probabilities are of great significance because incomplete answers may be the potential answers that interest the users. Different from complete evaluation, evaluating incomplete twigs in probabilistic XML introduces some new challenges. On one hand, incomplete queries do not only obtain complete matches, but also return answers that contain considerable incomplete matches. On the other hand, the processing of incomplete evaluation is more complicated. It is obvious that a ranking approach should be adopted along with evaluating incomplete answers. In this paper, we propose an efficient algorithm to handle the problem of querying incomplete twigs over the probabilistic XML database. We also present a novel algorithm for ranking the incomplete answers. The experimental results show that our proposed algorithms can improve the performance of querying and ranking incomplete twigs significantly.