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This paper proposes a relational calculus (RC/S) which uses set comparison and set manipulation operators to replace universal quantifiers and negations (with the exception of formulas of type ...R(x), i.e., "x is not in R"). We argue that, compared to the relational calculus (RC) of Codd, RC/S queries are easier to construct and easier to comprehend. We prove that the expressive power of RC is equivalent to the expressive power of RC/S, and give algorithms for translating an RC query into an RC/S query and vice versa. We define a safe RCIS query as one that has finite output and that can be evaluated in finite time. We then define a subset of RC/S queries, called RC/S*, and prove that RC/S* is safe. RC/S* is compared to the existing largest safe subsets of RC, i.e., the evaluable formulas and the allowed formulas. We give algorithms to transform any evaluable formula into an RC/S* query. Also, some RC/S* formulas that are not evaluable are given. RC/S* queries can be directly implemented using a graphical language similar to Query-by-Example (QBE). We briefly describe two different graphical languages that are equivalent, to the RC/S* in expressive power, and compare these languages to the QBE. [ABSTRACT FROM AUTHOR]

Databases that are mainly used for statistical analysis are called statistical databases (SDB). A statistical database management system (SDBMS) may be defined as a database management system that provides capabilities 1) to model, store, and manipulate data in a manner suitable for the needs of SDB users, and 2) to apply statistical data analysis techniques that range from simple summary statistics to advanced procedures. This paper surveys the existing and proposed SDB data definition and data manipulation (i.e., query) languages. [ABSTRACT FROM AUTHOR]

A statistical database (SDB) may be defined as an ordinary database with the capability of providing statistical information to user queries. The security problem for the SDB is to limit the use of the SDB so that only statistical information is available and no sequence of queries is sufficient to infer protected information about any individual. When such information is obtained, the SDB is said to be compromised. Inference control mechanisms are internal protection mechanisms applied to SDB's. Many researchers have studied different protection mechanisms to prevent an SDB from being compromised. However, most of these mechanisms are either ineffective or inefficient or are only applicable to large SDB's. Auditing in SDB's is initially proposed in the form of investigating log trails manually. In this paper, we present a practical technique for managing the past history of user's queries, discuss how the sequence of all the answered queries of the SDB can be reduced and stored in finite storage, and describe how this storage scheme can provide an effective way of checking compromise. We believe that this will help us develop a more practical and efficient tool for protection in a small SDB than the previously known mechanisms. Further, we extend the idea to checking compromise of a set of queries in a more efficient way than one query at a time. We also show that the problem of maximizing the amount of information to the users without compromising the SDB is NP-complete. [ABSTRACT FROM AUTHOR]

The security problem of a statistical database is to limit database use so that no private information is deducible. This paper discusses the advantages of using a Question-Answering System and a security kernel to enhance the security constraints at the conceptual model level. An SDB design with the goal of helping the OBA in specifying certain security constraints is proposed. [ABSTRACT FROM AUTHOR]

Time-by-Example (TBE) is a user-friendly query language designed specifically for historical relational databases. It follows the graphical structure and the example query concept of QBE, and employs the hierarchical arrangement of subqueries of Abe and STBE. Similar to STBE, TBE handles set- and simple-valued attributes. In addition, to handle time, TBE is capable of manipulating triplet- and set-triplet-valued attributes. The underlying data model used in TBE is an extended relational data model in which nonfirst normal form relations and attribute time stamping (in contrast to tuple time stamping) are used. A triplet is a 3-tuple whose components are the lower and upper bounds of a time interval and a value valid over the interval. A triplet is used as a time- stamped value of a time-dependent attribute. Set-valued time-dependent attributes are modeled by sets of triplets. To process TBE queries and to define a historical relational algebra (HRA), standard operators of the relational algebra and the pack/unpack operators of [21] are augmented by triplet-decomposition, triplet- formation, slice, and drop-time operators. Methodologies for translating TBE queries into HRA expressions and for constructing their parse trees are presented. [ABSTRACT FROM AUTHOR]