Answers to queries concerning uncertain and imprecise information in fuzzy relational databases

A model of an extended fuzzy relational database was proposed to accommodate uncertain and imprecise information. We use two supplementary measurements, satisfactory degree and extra degree, for determining the quality of answers to Select‐Project‐Join (SPJ) queries. The method of measurement determines how much satisfactory information is provided and how much truth information is required for a query. The answers to the query thus contain sure answers and maybe answers. The core of this study is the detailed discussion on the quality of answers in an extended fuzzy relation to query processing. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 647–668, 2005.     

Translation of fuzzy queries for relational database system

The concept of a database skeleton which reflects both the user's conception of the real world and the system's understanding of the interrelationships among database entities is described. It consists of a conceptual schema (conceptual graphs) and a relational schema (information graph). With the aid of the database skeleton, fuzzy queries can be translated and disambiguated by analyzing the queries using the conceptual graphs of a database skeleton. The query language XQL is introduced, and the XQL translator is described in some detail.     

Supporting ranking queries on uncertain and incomplete data

Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes introduces new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records’ scores induces a partial order over records, as opposed to the total order that is assumed in the conventional ranking settings. In this paper, we present a new probabilistic model, based on partial orders, to encapsulate the space of possible rankings originating from score uncertainty. Under this model, we formulate several ranking query types with different semantics. We describe and analyze a set of efficient query evaluation algorithms. We show that our techniques can be used to solve the problem of rank aggregation in partial orders under two widely adopted distance metrics. In addition, we design sampling techniques based on Markov chains to compute approximate query answers. Our experimental evaluation uses both real and synthetic data. The experimental study demonstrates the efficiency and effectiveness of our techniques under various configurations.     

A fuzzy object-oriented data model for managing vague and uncertain information

In this paper, a fuzzy Object-Oriented Data model (FOOD) is defined based on the extension of a Graph-based Object model (D. Lucarella and A. Zanzi “A graph-oriented data model,” in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer-Verlag, Berlin, 1996, pp. 197–206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation. ©1999 John Wiley & Sons, Inc.     

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Cloud computing enables a conventional relational database system's hardware to be adjusted dynamically according to query workload, performance and deadline constraints. One can rent a large amount of resources for a short duration in order to run complex queries efficiently on large-scale data with virtual machine clusters. Complex queries usually contain common subexpressions, either in a single query or among multiple queries that are submitted as a batch. The common subexpressions scan the same relations, compute the same tasks (join, sort, etc.), and/or ship the same data among virtual computers. The total time spent for the queries can be reduced by executing these common tasks only once. In this study, we build and use efficient sets of query execution plans to reduce the total execution time. This is an NP-Hard problem therefore, a set of robust heuristic algorithms, Branch-and-Bound, Genetic, Hill Climbing, and Hybrid Genetic-Hill Climbing, are proposed to find (near-) optimal query execution plans and maximize the benefits. The optimization time of each algorithm for identifying the query execution plans and the quality of these plans are analyzed by extensive experiments.     

A group-select operation for relational algebra and implicationsfor database machine design

A group-select operation has been defined for relational algebra. This operation is found to be useful for efficiently reducing expressions of nonprocedural relational languages that permit natural quantifiers. Conceptually, the operation first partitions a relation into blocks of tuples that have the same value for an attribute or attribute concatenation. It then extracts each block for which a specified number of tuples meet a specified condition. The quantity of tuples for the operation is specified by means of a natural quantifier. Performance of the group-select operation will be poor with conventional file processing, making the operation more suitable for use with a database machine with an associative memory     

A relational database machine with large semiconductor disk and hardware relational algebra processor

This paper describes the basic concepts, design and implementation decisions, standpoints and significance of the database machine Delta in the scope of Japan’s Fifth Generation Computer Project. Delta is planned to be operational in 1985 for researchers’ use as a backend database machine for logic programming software development. Delta is basically a relational database machine system. It combines hardware facilities for efficient relational database operations, which are typically represented by relational algebra, and software which deals with hardware control and actual database management requirements. Notable features include attribute-based internal schema in accordance with the characteristics found in the relation access from logic programming environment. This is also useful for the hardware relational algebra manipulation algorithm based on merge-sorting of attributes by hardware and a large capacity Semiconductor Disk for fast access to databases. Various implementation decisions of database management requirements are made in this novel system configuration, which will be meaningful to give an example for constructing a hardware and software combination of a relational database machine. Delta is in the stage between detailed design and implementation.     

Alpha: an extension of relational algebra to express a class ofrecursive queries

An extension of E.F. Codd's relational algebra (1970) with an alpha (α) operator is presented that allows a large class of natural and useful recursive queries to be expressed, and yet has the property of being efficiently implementable. Formally, this class is a superset of linear recursive queries. Intuitively, this class comprises queries that examine transitive relationships between various instances of an entity. It is believed that this class covers many natural and interesting recursive queries. Examples of such queries include determining parts requirements for manufacturing a product, finding the critical path in a project management network, finding the shortest path between two cities, verifying connectivity between two points of a circuit, etc     

Rewriting rules to permeate complex similarity and fuzzy queries within a relational database system

In recent years, the availability of complex data repositories (e.g., multimedia, genomic, semistructured databases) has paved the way to new potentials as to data querying. In this scenario, similarity and fuzzy techniques have proven to be successful principles for effective data retrieval. However, most proposals are domain specific and lack of a general and integrated approach to deal with generalized complex queries, i.e., queries where multiple conditions are expressed, possibly on complex as well as on traditional data. To overcome such limitations, much work has been devoted to the development of middleware systems to support query processing on multiple repositories. On a similar line, We present a formal framework to permeate complex similarity and fuzzy queries within a relational database system. As an example, we focus on multimedia data, which is represented in an integrated view with common database data. We have designed an application layer that relies on an algebraic query language, extended with MM-tailored operators, and that maps complex similarity and fuzzy queries to standard SQL statements that can be processed by a relational database system, exploiting standard facilities of modern extensible RDBMS. To show the applicability of our proposal, we implemented a prototype that provides the user with rich query capabilities, ranging from traditional database queries to complex queries gathering a mixture of Boolean, similarity, and fuzzy predicates on the data.     

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.     

Methods for parallel execution of complex database queries

During the last decade, all commercial database systems have included features for parallel processing into their products. This development has been driven by the fact that databases grow in size at considerable rates. According to the results of the 1998 `very large database contest' the world’s largest databases, which have reached a size of over 10TB, double in size every year. At that speed, they outgrow the increase in processor speed and memory size, so additional measures are required to accommodate the effects of rapidly growing volumes of data. Parallelism is one of those options. It helps to keep processing times constant, even if the size of the database increases. That effect, which is often referred to as `scaleup' is important for loading, index creation, all kinds of administrative operations on the database, and of course for long batch-type applications. Parallelism is also employed to speed-up queries that otherwise would take days or weeks to process and thus would be useless for the application. This type of requirement: fast results of complex queries on large data sets is characteristic of decision support applications. In this overview we will explain how parallelism in databases can help to solve such problems.     

Evaluation of queries based on the extended relational calculi

An efficient database search algorithm is presented. Four major enhancements on the preceding works have been made. They are (1) relational calculus is extended to enable processing an arbitrary logical function defined on one or more relations, (2) a set of elementary operations which are similar to but are more efficient in processing compound search conditions than the relational algebra is used, (3) the target list processing is completely separated from the search process, and (4) sequential collation procedure is fully utilized to deal with conditions of a certain type defined on two or more relations. The algorithm is composed of two parts: syntactical transformation of the given extended relational calculus and the search execution. Various optimization issues are integrated into these two parts.     

Measuring the quality of queries in the fuzzy relational databases

In this paper, we develop a new method to measure the quality of each tuple as an answer with respect to Select‐Project‐Join (SPJ) queries so that we can determine which answers are better answers to the given query in a fuzzy relational database. The quality of an answer is viewed as how much sure information is provided, and how much extra information is needed so that it will be a sure answer to the query. The less extra information that is required and the more sure information that is provided by an answer, the higher the quality of that answer is, and in consequence, it will be more reliable. © 2001 John Wiley & Sons, Inc.     

A functional processor for the relational algebra on a microcomputer

We have built on the U.C.S.D. P-system (running on an IBM Personal Computer) a relational algebra processor, MRDS/FS, which is extremely powerful and which supports a functional syntax for the programmer-user. The relational algebra is provided in the extended operators μ-join, σ-join, project and select. The domain algebra is fully implemented for the first time, giving operations on attributes: arithmetic, logic, comparison and four different categories of aggregation of these. A strictly functional syntax is provided, permitting user-defined functions using the relational and domain algebras as primitive operations. An interactive editor permits the creation, copying and changing of both relations and user-defined functions.     

Fuzzy relational algebra for possibility-distribution-fuzzy-relational model of fuzzy data

In the real world, there exist a lot of fuzzy data which cannot or need not be precisely defined. We distinguish two types of fuzziness: one in an attribute value itself and the other in an association of them. For such fuzzy data, we propose a possibility-distribution-fuzzy-relational model, in which fuzzy data are represented by fuzzy relations whose grades of membership and attribute values are possibility distributions. In this model, the former fuzziness is represented by a possibility distribution and the latter by a grade of membership. Relational algebra for the ordinary relational database as defined by Codd includes the traditional set operations and the special relational operations. These operations are classified into the primitive operations, namely, union, difference, extended Cartesian product, selection and projection, and the additional operations, namely, intersection, join, and division. We define the relational algebra for the possibility-distribution-fuzzy-relational model of fuzzy databases.     

Robust control scheme for uncertain semi-Markov jump systems subject to digital communication constraint and incomplete sojourn-time information

This paper deals with the robust control problem for networked uncertain semi-Markov jump systems over digital communication channel, where some complicated factors are considered in this framework, such as false data injection attacks, incomplete sojourn-time information, and actuator fault. In network environment, these problems frequently occurred: (1) Data must be quantified before it is transmitted via digital communication channels, and (2) there is always the risk of cyber-attack. Traditional controllers are not effective to solve the robust control design issue of uncertain semi-Markov jump systems since the signal quantization and cyber-attack will degrade control performance evidently. Then, a resilient controller is designed to guarantee the stable running of system with incomplete sojourn-time information and the system resiliency to unsecure shared data. With the help of Lyapunov stability theory, sufficient conditions are derived in the form of linear matrix inequality, which can determine the gains of the resilient controller. Finally, the simulation examples about single-machine infinite-bus power system are given to explain the effectiveness and feasibility of the proposed control approach.     

面向内容认证的关系数据库水印技术研究

针对现有水印算法的不足,提出了一种新的脆弱性关系数据库水印算法.在本算法中,由水印密钥控制元组的划分和水印的生成,通过构造水印矩阵对关系数据遭受的篡改进行定位和描述.实验结果和理论分析表明,水印生成方法安全有效,可实现水印的盲检测.水印嵌入引起的数据误差较小,算法具有较好的可行性.     

Grey relational analysis method for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

With respect to 2-tuple linguistic multiple attribute group decision making problems with incomplete weight information, some basic concepts and operational laws of 2-tuple linguistic variables are introduced. An optimization model based on the maximizing deviation method, by which the attribute weights can be determined, is established. According to the traditional ideas of grey relational analysis (GRA), the optimal alternative(s) is determined by calculating the linguistic degree of grey relation of every alternative and 2-tuple linguistic positive ideal solution and 2-tuple linguistic negative ideal solution. It is based on the concept that the optimal alternative should have the largest degree of grey relation from positive ideal solution and the smallest degree of grey relation from the negative ideal solution. The method has exact characteristic in linguistic information processing. It avoided information distortion and losing which occur formerly in the linguistic information processing. Finally, a numerical example is used to illustrate the use of the proposed method. The result shows the approach is simple, effective and easy to calculate.