Addendum to Null values in nested relational databases

We define a new operator, decomposition projection, and show that extended projection is a precise generalization of decomposition projection with respect to unnesting, and that null-extended projection is a precise generalization of decomposition projection with respect to unnesting and PNF possibility function POSS.     

Null values in fuzzy databases

Since in the real world, it often occurs that information is missing, database systems clearly need some facilities to deal with missing data. With respect to traditional database systems, the most commonly adopted approach to this problem is based on null values and three valued logic. This paper deals with the semantics and the use of null values in fuzzy databases. In dealing with missing information a distinction is made between incompleteness due to unavailability and incompleteness due to inapplicability. Both the database modelling and database querying aspects are described. With respect to attribute values, incompleteness due to unavailability is modelled by possibility distributions, which is a commonly used technique in the fuzzy databases. Domain specific null values, represented by a bottom symbol, are used to model incompleteness due to inapplicability. Extended possibilistic truth values are used to formalize the impact of data manipulation and (flexible) querying operations in the presence of these null values. The different cases of appearances of null values in the handling of selection conditions of flexible database queries are described in detail.     

Correction to Null values in nested relational databases by M.A. Roth,H.F. Korth,and A. Silberschatz

We show that the extended projection of a nested relation in Partitioned Normal Form (PNF) is not a precise generalization of standard projection with respect to unnesting and PNF possibility functionPOSS.     

An efficient method for estimating null values in relational databases

Generally, a database system containing null value attributes will not operate properly. This study proposes an efficient and systematic approach for estimating null values in a relational database which utilizes clustering algorithms to cluster data, and a regression coefficient to determine the degree of influence between different attributes. Two databases are used to verify the proposed method: (1) Human resource database; and (2) Waugh's database. Furthermore, the mean of absolute error rate (MAER) and average error are used as evaluation criteria to compare the proposed method with other methods. It demonstrates that the proposed method is superior to existing methods for estimating null values in relational database systems. Jia-Wen Wang was born on September 5, 1978, in Taipei, Taiwan, Republic of China. She received the M.S. degree in information management from the National Yunlin University of Science and Technology, Yunlin, Taiwan, in 2003. Since 2003, she has been a PhD degree student in Information Management Department at the National Yunlin University of Science and Technology. Her current research interests include fuzzy systems, database systems, and artificial intelligence. Ching-Hsue Cheng received the B.S. degree in mathematics from Chinese Military Academy, Taiwan, in 1982, the M.S. degree in applied mathematics from the Chung Yuan Christian University, Taiwan, in 1988, and the Ph.D. degree in system engineering and management from National Defence University, Taiwan, in 1994. Currently, he is a professor of the Department of Information Management, National YunLin University of Technology & Science. His research interests are in decision science, soft computing, software reliability, performance evaluation, and fuzzy time series. He has published more than 120 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with government, and other research-sponsoring agencies.     

Keyword search in relational databases

This paper surveys research on enabling keyword search in relational databases. We present fundamental characteristics and discuss research dimensions, including data representation, ranking, efficient processing, query representation, and result presentation. Various approaches for developing the search system are described and compared within a common framework. We discuss the evolution of new research strategies to resolve the issues associated with probabilistic models, efficient top-k query processing, and schema analysis in relational databases.     

XML数据库和关系数据库之比较

XML已经成为数据表示和交换的数据格式标准。随着大量XML文档的出现,应用数据库技术实现对XML数据的管理引起了越来越多研究者的兴趣。作为研究XML数据库技术的一个开始点,通过与关系数据库比较,可以深刻理解XML数据库与关系数据库的异同,进而为解决XML数据库所面临的问题,如为数据冗余控制、并发访问控制等提供必要的基础。两种数据库的比较是从数据模型、查询路径、完整性约束和规范化5个方面进行的,由于数据模型是数据库的基石,二者的数据模型从构造机制、名字的惟一性、空值、实体标识、实体问关系、文档顺序、数据结构的规则性、递归、数据自描述性等9个方面进行了详细讨论。     

Object databases as generalizations of relational databases

One attractive approach to object databases is to see them as potentially an evolutionary development from relational databases. This paper concentrates on substantiating the technical basis for this claim, and illustrates it in some detail with an upwards-compatible extension of ANSI SQL2 for conventional objects. This could serve as a foundation for the development of higher-level facilities for more complex objects.     

An interface between object-oriented databases and relational databases

Currently relational databases are widely used, while object-oriented databases are emerging as a new generation of database technology. This paper presents a methodology to provide effective sharing of information in object-oriented databases and relational databases. The object-oriented data model is selected as a common data model to build an integrated view of the diverse databases. An object-oriented query language is used as a standard query language. A method is developed to transform a relational data definition to an equivalent object-oriented data definition and to integrate local data definitions. Two distributed query processing methods are derived. One is for general queries and the other for a special class of restricted queries. Using the methods developed, it is possible to access distributed object-oriented databases and relational databases such that the locations and the structural differences of the databases are transparent to users.     

Discovering causal rules in relational databases

This article explores the combined application of inductive learning algorithms and causal inference techniques to the problem of discovering causal rules among the attributes of a relational database. Given some relational data each field can be considered as a random variable and a hybrid graph can be built by detecting conditional independencies among variables. The induced graph represents genuine and potential causal relations as well as spurious associations. When the variables are discrete or have been discretized to test condi tional independencies supervised induction algorithms can be used to learn causal rules that is conditional statements in which causes appear as antecedents and effects as consequences. The approach is illustrated by means of some experiments conducted on different data sets.     

Unacceptable components in fuzzy relational databases

The existence of unacceptable components, which consist of unacceptable tuples and elements in attribute values, is shown in fuzzy relational databases. The unacceptable components are created by update operations, insertion, deletion, and modification. An unacceptable tuple in a relation is a tuple such that the degree of its not belonging to that relation is greater than that of its belonging to. The unacceptable tuple can be easily eliminated from relations. There are three kinds of unacceptable elements. One case of unacceptable elements is a redundant element created by insertion and modification. Another is an element created by a possible tuple value not at all or partially satisfying integrity constraints in insertion and modification. The other is an element created by a possible tuple value completely or partially satisfying update conditions in deletion. The unacceptable elements can be eliminated from relations without loss of information. As a result, we can obtain fuzzy relational databases without unacceptable components by a reasonable way. © 1996 John Wiley & Sons, Inc.     

Set-oriented data mining in relational databases

Data mining is an important real-life application for businesses. It is critical to find efficient ways of mining large data sets. In order to benefit from the experience with relational databases, a set-oriented approach to mining data is needed. In such an approach, the data mining operations are expressed in terms of relational or set-oriented operations. Query optimization technology can then be used for efficient processing.

In this paper, we describe set-oriented algorithms for mining association rules. Such algorithms imply performing multiple joins and thus may appear to be inherently less efficient than special-purpose algorithms. We develop new algorithms that can be expressed as SQL queries, and discuss optimization of these algorithms. After analytical evaluation, an algorithm named SETM emerges as the algorithm of choice. Algorithm SETM uses only simple database primitives, viz., sorting and merge-scan join. Algorithm SETM is simple, fast, and stable over the range of parameter values. It is easily parallelized and we suggest several additional optimizations. The set-oriented nature of Algorithm SETM makes it possible to develop extensions easily and its performance makes it feasible to build interactive data mining tools for large databases.     

Dynamic non-dense indexes in relational databases

Indexes are a commonly used structure that provides fast access to the data. Their use imply storage and maintenance costs. This paper presents a technique to reduce index size, based on the elimination of tuple offsets in the classical B⁺ tree structure. It is shown that this technique gives advantages both in the tuple access and index maintenance.     

Uncertainty in a nested relational database model

Some database models have already been developed to deal with complex values but they have constrains that data stored is precise and queries are crisp. However, as many researchers have pointed out, there is a need to present, manipulate, and query complex and uncertain data of various non-traditional database applications such as oceanography, multimedia, meteorology, office automation systems, engineering designs, expert database systems and geographic information systems. In this paper, we present a logical database model, which is an extension of a nested relational data model (also known as an NF² data model), for representing and manipulating complex and uncertain data in databases. We also introduce a possible physical representation of such complex and uncertain values in databases and describe the query processing of the model that we discuss here.     

Losslessness and project-join constructibility in relational databases

Summary Checking a database scheme for the lossless join property with respect to a set, M, of multivalued dependencies (MVDs) is NP-hard. We prove that, for a class of MVDs that includes the set of projected full MVDs, this check can be performed in polynomial time. Even with a lossless database scheme and a consistent database, joining the set of relations in the database can take time and space that is exponential in the size of the relation finally obtained. Joining the set of relations of such a database can be performed in polynomial time if the database scheme is project-join constructible with respect to M. We prove that project-join constructibility, a stricter condition than the lossless join property, can be detected in a database scheme in polynomial time.     

A study of homogeneity in relational databases

We define four different properties of relational databases which are related tothe notion of homogeneity in classical model theory. The main question for their definition is, for any given database to determine the minimum integer k, such that whenever two k-tuples satisfy the same properties which are expressible in first order logic with up to k variables (FO ^k ), then there is an automorphism which maps each of these k-tuples onto each other. We study these four properties as a means to increase the computational power of subclasses of the reflective relational machines (RRMs) of bounded variable complexity. These were introduced by S. Abiteboul, C. Papadimitriou and V. Vianu and are known to be incomplete. For this sake we first give a semantic characterization of the subclasses of total RRM with variable complexity k (RRM ^k ) for every natural number k. This leads to the definition of classes of queries denoted as Q C Q ^k . We believe these classes to be of interest in their own right. For each k>0, we define the subclass Q C Q ^k as the total queries in the class C Q of computable queries which preserve realization of properties expressible in FO ^k . The nature of these classes is implicit in the work of S. Abiteboul, M. Vardi and V. Vianu. We prove Q C Q ^k =total(RRM ^k ) for every k>0. We also prove that these classes form a strict hierarchy within a strict subclass of total(C Q). This hierarchy is orthogonal to the usual classification of computable queries in time-space-complexity classes. We prove that the computability power of RRM ^k machines is much greater when working with classes of databases which are homogeneous, for three of the properties which we define. As to the fourth one, we prove that the computability power of RRM with sublinear variable complexity also increases when working on databases which satisfy that property. The strongest notion, pairwise k-homogeneity, allows RRM ^k machines to achieve completeness.     

Detecting anomalous access patterns in relational databases

A considerable effort has been recently devoted to the development of Database Management Systems (DBMS) which guarantee high assurance and security. An important component of any strong security solution is represented by Intrusion Detection (ID) techniques, able to detect anomalous behavior of applications and users. To date, however, there have been few ID mechanisms proposed which are specifically tailored to function within the DBMS. In this paper, we propose such a mechanism. Our approach is based on mining SQL queries stored in database audit log files. The result of the mining process is used to form profiles that can model normal database access behavior and identify intruders. We consider two different scenarios while addressing the problem. In the first case, we assume that the database has a Role Based Access Control (RBAC) model in place. Under a RBAC system permissions are associated with roles, grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals while holding a specific role, behave differently than expected. An important advantage of providing an ID technique specifically tailored to RBAC databases is that it can help in protecting against insider threats. Furthermore, the existence of roles makes our approach usable even for databases with large user population. In the second scenario, we assume that there are no roles associated with users of the database. In this case, we look directly at the behavior of the users. We employ clustering algorithms to form concise profiles representing normal user behavior. For detection, we either use these clustered profiles as the roles or employ outlier detection techniques to identify behavior that deviates from the profiles. Our preliminary experimental evaluation on both real and synthetic database traces shows that our methods work well in practical situations. This material is based upon work supported by the National Science Foundation under Grant No. 0430274 and the sponsors of CERIAS.     

Algebraic equivalences of nested relational operators

Algebraic optimisation is both theoretically and practically important for query processing in complex value databases. In this paper, we consider this issue and investigate some algebraic properties concerning the nested relational operators.The join operation is one of the most time-consuming operations in nested relational query processing. We introduce a new join operator, called P-join, which combines the advantages of Roth's extended natural join and Colby's recursive join for efficient data access. We also investigate some algebraic properties concerning the P-join operator and extended relational operators, which can be used for query optimisation in nested relational databases.We then examine the role of the restructuring operators nest and unnest in their interactions with the extended relational operators proposed by Roth et al. Under certain functional and mutual data dependencies, the six nested relational equations will hold.Finally, we outline the steps of a heuristic optimisation algorithm that utilises algebraic transformation rules developed in this paper and previous related work to transform an initial query to an optimised one that is more efficient to execute.     

Symbolic user-defined periodicity in temporal relational databases

Calendars and periodicity play a fundamental role in many applications. Recently, some commercial databases started to support user-defined periodicity in the queries in order to provide "a human-friendly way of handling time" (see, e.g., TimeSeries in Oracle 8). On the other hand, only few relational data models support user-defined periodicity in the data, mostly using "mathematical" expressions to represent periodicity. In this paper, we propose a high-level "symbolic" language for representing user-defined periodicity which seems to us more human-oriented than mathematical ones, and we use the domain of Gadia's temporal elements in order to define its properties and its extensional semantics. We then propose a temporal relational model which supports user-defined "symbolic" periodicity (e.g., to express "on the second Monday of each month") in the validity time of tuples and also copes with frame times (e.g., "from 1/1/98 to 28/2/98"). We define the temporal counterpart of the standard operators of the relational algebra, and we introduce new temporal operators and functions. We also prove that our temporal algebra is a consistent extension of the classical (atemporal) one. Moreover, we define both a fully symbolic evaluation method for the operators on the periodicities in the validity times of tuples, which is correct but not complete, and semisymbolic one, which is correct and complete, and study their computational complexity.