An evaluation of RQL

We define a language G for querying data represented as a labeled graph G. By considering G as a relation, this graphical query language can be viewed as a relational query language, and its expressive power can be compared to that of other relational query languages. We do not propose G as an alternative to general purpose relational query languages, but rather as a complementary language in which recursive queries are simple to formulate. The user is aided in this formulation by means of a graphical interface. The provision of regular expressions in G allows recursive queries more general than transitive closure to be posed, although the language is not as powerful as those based on function-free Horn clauses. However, we hope to be able to exploit well-known graph algorithms in evaluating recursive queries efficiently, a topic which has received widespread attention recently.

PIQUE: A relational query language without relations

Most data stream management systems are based on extensions of the relational data model and query languages, but rigorous analyses of the problems and limitations of this approach, and how to overcome them, are still wanting. In this article, we elucidate the interaction between stream-oriented extensions of the relational model and continuous query language constructs, and show that the resulting expressive power problems are even more serious for data streams than for databases. In particular, we study the loss of expressive power caused by the loss of blocking query operators, and characterize nonblocking queries as monotonic functions on the database. Thus we introduce the notion of NB -completeness to assure that a query language is as suitable for continuous queries as it is for traditional database queries. We show that neither RA nor SQL are NB -complete on unordered sets of tuples, and the problem is even more serious when the data model is extended to support order—a sine-qua-non in data stream applications. The new limitations of SQL, compounded with well-known problems in applications such as sequence queries and data mining, motivate our proposal of extending the language with user-defined aggregates (UDAs). These can be natively coded in SQL, according to simple syntactic rules that set nonblocking aggregates apart from blocking ones. We first prove that SQL with UDAs is Turing complete. We then prove that SQL with monotonic UDAs and union operators can express all monotonic set functions computable by a Turing machine ( NB -completeness) and finally extend this result to queries on sequences ordered by their timestamps. The proposed approach supports data stream models that are more sophisticated than append-only relations, along with data mining queries, and other complex applications.

The National Center for Biotechnology Information (NCBI) has created a database collection that includes several protein and nucleic acid sequence databases, a biosequence-specific subset of MEDLINE, as well as value-added information such as links between similar sequences. Information in the NCBI database is modeled in Abstract Syntax Notation 1 (ASN.1) an Open Systems Interconnection protocol designed for the purpose of exchanging structured data between software applications rather than as a data model for database systems. While the NCBI database is distributed with an easy-to-use information retrieval system, ENTREZ, the ASN.1 data model currently lacks an ad hoc query language for general-purpose data access. For that reason, we have developed a software package, SORTEZ, that transforms the ASN.1 database (or other databases with nested data structures) to a relational data model and subsequently to a relational database management system (Sybase) where information can be accessed through the relational query language, SQL. Because the need to transform data from one data model and schema to another arises naturally in several important contexts, including efficient execution of specific applications, access to multiple databases and adaptation to database evolution this work also serves as a practical study of the issues involved in the various stages of database transformation. We show that transformation from the ASN.1 data model to a relational data model can be largely automated, but that schema transformation and data conversion require considerable domain expertise and would greatly benefit from additional support tools.

Hierarchical part-of relationships/aggregation structures and related queries are essential parts of information systems. However, relational database query languages do not explicitly support hierarchical relationships and queries. A hierarchical query may require a great number of join operations, which increases the effort in query formulation. Therefore, we propose path expressions in formulating hierarchical views over relational data because path expressions are a conventional and compact way to represent hierarchical relationships. We embed path expressions within SQL queries and compile them to standard SQL. This ensures that the path expressions can straightforwardly be implemented on the top of standard relational database systems. The compilation of a path expression is given by an attribute grammar, a conventional formalism to define the semantics of a language.

Numerous proposals have been made to extend the relational database query language SQL to serve as a spatial query language and currently efforts are under way to establish a standardized spatial SQL. Here it is argued that the SQL framework is inappropriate for an interactive spatial query language and an extended spatial SQL is at best a short-term solution. The serious deficiencies of any spatial SQL are divided into two groups: the severe difficulties to incorporate necessary spatial concepts into SQL such as graphical display and its specification and the lack of power within the relational framework with missing support for complex objects, object identity, meta queries, and non-first-order logic—all crucial when dealing with spatial data.

A Fuzzy Query Language (FQL) for relational databases is proposed. FQL, developed as an extension of relational domain calculus, has sufficient capabilities to express all five types of fuzzy statements distinguished and represented in a meaning representation language PRUF by 7aieh. The main idea for developing FQL lies in a method that harmoniously integrates relational domain calculus and PRUF, together with a natural determination method of matching degrees of domain values in relational databases against FQL queries. Effects of FQL are twofold. One is practical. We expect that FQL will add a human-oriented interface to currently widespread relational databases all over the world that have already stored a vast amount of useful information. In addition, fuzzy expert systems currently under development can be designed to support FQL so that they can make use of fact data stored in some relational database in their processing. The other but principal effect is theoretical. FQL provides a theoretical framework that can play a kernel role in integrating many previous-and-future fuzzy extensions of relational database query languages systematically into a unified fuzzy query language.KeywordsRelational databasesquery languagefuzzy propositionsfuzzy well-formed formulasrelational domain calculusmatching degrees

The relational approach has been applied very literally to temporal databases. For example, the SALARY and DEPT of an employee do not necessarily change at the same time, resulting in a split of information into two tables: NAME SALARY START END and NAME DEPT START END, where START and END are instants of time, treated as special attributes. A unit of information, such as salary of an employee, is further split into several tuples. Normal forms, dependencies and other nice concepts of relational databases remain inapplicable to the temporal case. We propose that a tuple, the fundamental molecular unit of information, be viewed as a function from a union of finitely many intervals of time into domains of attributes. This eliminates the problems stated above. It gives rise to elegant relational algebra, tuple calculus and other query languages and brings temporal databases within the classical framework. It is hoped that this model will draw the attention of researchers to temporal databases.

Research in databases has played a vital role in developing information system technology and also the outcome of this research has been very successful resulting in great potential for industry. The database industry is generating billions of dollars of business annually. It has estimated that the database industry itself has generated $42 billion revenue in 2000 and this market is growing at 11% annually. In the software industry, it is second only to operating system software. The evolution of database systems started in late 60's when hierarchical and network data models were developed, but these models did not get much momentum because they were not suited for complex applications [9]. In early 70's, Ted Codd proposed a relational data model (for which he was awarded a Turing award) which became the backbone of developing database applications. Although this model was criticized by COBOL/CODASYL group people, it became very popular because of its simplicity. In late 70's and early 80's most of the research work was focused on developing fundamentals of relational database theory, query languages, transaction management and query optimization. In late 80's and early 90's, the database research community grew exponentially and made several breakthrough in many areas like object-oriented, active, deductive, and parallel and distributed databases. The research ideas in these areas have been implemented successfully in different database vendors' products. In early 90's, most of the businesses realize that their business is becoming very competitive and they need some sophisticated tools that can analyze their business data, customers profiles and product information so that they can improve their marketing strategy and management of organization. Data mining and data warehousing technology was developed for satisfying such needs [1].

Section 3.1 describes the relational data model, which is a particular data abstraction that uses relations or tables to represent the data in a database. Section 3.2 introduces the relational algebra query language, while Section 3.3 introduces the Structured Query Language (SQL) for relational databases. Finally, Section 3.4 discusses the evaluation of relational database queries.KeywordsRelational DatabaseQuery LanguageRelational AlgebraSocial Security NumberStructure Query LanguageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Due to the intrinsic characteristics of the relational model, the standard database user interface forces users to be familiar with the database schema and underlying data to improve the efficiency of information retrieval. It proposes an intelligent query processing based on Attribute-Oriented Generalization (AOG) to better user experience during the information retrieval process. Firstly, it adds semantics discarded by the relational model to raw data through attribute-oriented generalization, and builds instances of Type Abstraction Hierarchy (TAH). Secondly, it constructs the knowledge base, which is designed on a relational mode, so that both the knowledge base and the underlying relational database can be handled in a single formalism by a relational query language. Thirdly, with the derived specific knowledge base incorporated into the underlying database, a prototype intelligent, interactive and straightforward query processing system with B/S architecture has been built at the top of SQL, which returns the semantically neighboring values and higher level more abstract values through the instance of TAH according to the results of the interaction with the user. Finally, three practical query examples are presented to further exemplify the main ideas and demonstrate the usefulness of the proposed query answering processes.

In this paper, we develop techniques for interoperable query processing between object and relational schemas. The objective is to pose a query against a local object schema and be able to share information transparently from target relational databases. Our approach is a mapping approach (as opposed to a global schema approach) and is based on using canonical representations (CR). We use one CR for resolving heterogeneity based on the object and relational query languages. We use a second parameterized CR to resolve representational heterogeneity between object and relational schema, and to build a mapping knowledge dictionary. There is also a set of mapping rules, based on the parameters of the CR, which defines the appropriate mapping between schemas. A query posed against the local object schema is first represented in the CR for queries, and then transformed by the mapping rules, to an appropriate query for the target relational schema, using relevant information from the mapping knowledge dictionary. The use of the parameterized CR allows us to build the mapping knowledge dictionary easily, and allows reusability of the mapping rules.

Temporal databases have been a research subject for many years, though one might wonder why so much attention has been devoted to this topic; indeed, time can easily be introduced as additional attributes in the relational model. Of course, specific operations need to be performed with temporal attributes, such as dealing with date formats, but such operations are not really problematic, and have long been available in commercial systems. Nevertheless, the feeling remains that time is special. Clearly, introducing time in a database requires some thought: Is one going to deal with valid time, transaction time, or both; is the basic unit a point or an interval; is time continuous or discrete; if discrete, what is the basic granularity? Nevertheless, once these questions have been answered, a direct representation by additional attributes appears at first to be adequate. With respect to querying, however, the need to express constraints on the time information arises in many cases, for instance in a query such as “which projects have overlapping schedules?” Moreover, when looking at the evaluation of queries, one notices that specific operations, such as compacting overlapping intervals, are necessary but are not directly supported in relational query languages.

The relational database query language SQL (originally, SEQUEL) was first developed as a non-procedural language: the user should specify only what data is desired, leaving it to the the system's query optimizer to determine how the data is accessed [ASTR 75]. IBM pioneered the development of query optimizer technology that would achieve this ideal, beginning with the well-known optimizer of System R [SELI 79], the prototype relational database management system (DBMS) developed at IBM Research Laboratory in San Jose, California during the late 1970's. The IBM products SQL/DS [SQL 84] and DB2 [CHEN 84], as well as the experimental distributed DBMS prototype R∗ [LOHM 85], contain optimizers that are based upon the System R prototype.

One of the main tasks set by the President of the Russian Federation to the Government in May 2018 is the development of small and medium-sized businesses. In recent years, the number of small business organizations in the Crimea has grown significantly, which, on the one hand, develops the economic sector of the Republic and provides jobs, and on the other, creates the fiercest competition in a market economy. So, only in the Crimea, more than 90% of small businesses sell their services to consumers, and are not producers of goods. In addition, the services offered by such companies are not exclusive in the Crimean market. For a company to be competitive, it needs to be recognized and be ahead when compared to other companies. In fact, to ensure competitiveness, a small business company must have a stable positive image, be familiar to everybody. Otherwise, the lifetime of such a company is limited from 1 year to 3 years. The purpose of the article is to describe the results of the study of the corporate culture of small business organizations. Materials and methods. To diagnose the corporate culture of the organization, we used the OSAI method; to determine the styles and norms of behavior, to identify the leading motives of employees' activities – the method of diagnosing the socio psychological attitudes of the individual in the motivational- need sphere developed by O.F. Potemkina. In the course of the study of 7 Crimean small business enterprises, the authors identified several groups of enterprises with tendencies to different types of corporate culture. The motivational profiles of the enterprises included in the ascertaining experiment were studied. During the discussion of the research procedure and its results there were questions concerning the cumbersomeness of psychodiagnostic techniques that measure the type and level of formation of corporate culture, as well as determining the criteria for the success of small businesses with different types of corporate culture in the market.