Complete Query Research Articles

Identification of novel homologues of three low molecular weight subunits of the mitochondrial bc1 complex.

Large-scale random cDNA sequencing projects have been started for several organisms and are a valuable tool for the analysis of quantitative and qualitative aspects of gene expression. However, the reliability of the obtained data is limited as most of the clones are only partially analysed on one strand. As a consequence the sequence entries derived from random cDNA sequencing projects usually comprise incomplete open reading frames. They nevertheless define complete and reliable coding sequences, if two prerequisites are fulfilled: (i) the clones encode very small proteins, and (ii) the clones have a high frequency in the cDNA-banks. The present study describes the use of cDNA databases for the identification of homologues of three low-molecular-weight subunits of the mitochondrial bc1 complex, termed the QCR6, QCR9 and QCR10 proteins. These polypeptides are only characterized for a small number of organisms, have a scarcely defined function and exhibit a low degree of structural conservation if compared between different species. Several clones were identified for each polypeptide by searches with TBLASTN using the known sequences as probes. Most of the database entries contain complete open reading frames and sequencing queries could be excluded due to the abundancy of the clones. Multiple sequence alignments are presented for all three polypeptides and consensus sequences are given which may provide a basis for the investigation of the proteins by site-directed mutagenesis.

G-Log: a graph-based query language

We introduce G-Log, a declarative query language based on graphs, which combines the expressive power of logic, the modeling power of complex objects with identity and the representation power of graphs. G-Log is a nondeterministic complete query language, and thus allows the expression of a large variety of queries. We compare G-Log to well-known deductive database languages, and find that it is the only nondeterministic and computationally complete language that does not suffer from the copy-elimination problem. G-Log may be used in a totally declarative way, as well as in a "more procedural" way. Thus, it provides an intuitive, flexible graph-based formalism for nonexpert database users.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Comparison of methods for searching protein sequence databases

We have compared commonly used sequence comparison algorithms, scoring matrices, and gap penalties using a method that identifies statistically significant differences in performance. Search sensitivity with either the Smith-Waterman algorithm or FASTA is significantly improved by using modern scoring matrices, such as BLOSUM45-55, and optimized gap penalties instead of the conventional PAM250 matrix. More dramatic improvement can be obtained by scaling similarity scores by the logarithm of the length of the library sequence (In()-scaling). With the best modern scoring matrix (BLOSUM55 or JO93) and optimal gap penalties (-12 for the first residue in the gap and -2 for additional residues), Smith-Waterman and FASTA performed significantly better than BLASTP. With In()-scaling and optimal scoring matrices (BLOSUM45 or Gonnet92) and gap penalties (-12, -1), the rigorous Smith-Waterman algorithm performs better than either BLASTP and FASTA, although with the Gonnet92 matrix the difference with FASTA was not significant. Ln()-scaling performed better than normalization based on other simple functions of library sequence length. Ln()-scaling also performed better than scores based on normalized variance, but the differences were not statistically significant for the BLOSUM50 and Gonnet92 matrices. Optimal scoring matrices and gap penalties are reported for Smith-Waterman and FASTA, using conventional or In()-scaled similarity scores. Searches with no penalty for gap extension, or no penalty for gap opening, or an infinite penalty for gaps performed significantly worse than the best methods. Differences in performance between FASTA and Smith-Waterman were not significant when partial query sequences were used. However, the best performance with complete query sequences was obtained with the Smith-Waterman algorithm and In()-scaling.

QBI

QBI is an icon-based query processing and exploration facility for large distributed databases [3]. As opposed to other interactive query interfaces, it combines (1) a pure iconic specification, i.e., no diagrams of any form, only icon manipulation, with (2) intensional browsing or metaquery tools that assist in the formulation of complete queries without involving path specification or access to the actual data in the database.Path expressions are automatically generated by QBI and irrespective of their length, represented by a single icon, allowing for better use of the screen. It requires no special knowledge of the content of the underlying database nor understanding of the details of the database schema. Hence, QBI is domain independent and equally useful to both unsophisticated and expert users.

A sound and complete query evaluation for implicit predicate which is a semantic descriptor of unknown values

A sound and complete query evaluation for implicit predicate which is a semantic descriptor of unknown values

No IFs, ANDs, or ORs: A study of database querying

The difficulty of expressing database queries was examined as a function of the language used. Two distinctly different query methods were investigated. One used a standard database query language, SQL, requiring users to express an English query using a formal syntax and appropriate combinations of boolean operators. The second used a newly designed Truth-table Exemplar-Based Interface (TEBI), which only required subjects to be able to choose examplars from a system-generated table representing a sample database. Through users' choices of critical exemplars, the system could distinguish between interpretations of an otherwise ambiguous English query. Performance was measured by number correct, time to complete queries, and confidence in query correctness. Individual difference analyses were done to examine the relationship between subjects' characteristics and ability to express database queries. Subjects' performance was observed to be both better, and more resistant to variability in age and levels of cognitive skills, when using TEBI than when using SQL to specify queries. Possible reasons for these differences are discussed.

Pattern match reduction for Relational Production Language in the USL MMDBS

The Relational Production Language, RPL, is a rule language where both the condition and action part of a rule is expressed in a relational language. This makes RPL a natural choice for expert database systems. In this paper, the performance of the RPL interpreter is addressed through the use of Pattern Match Reduction, PMR, within the context of the USL main memory database system, the USL MMDBS. PMR provides a generalization of relational view materialization by supporting relationally complete queries and by allowing the materialization of any/all intermediate nodes in a query tree. The USL MMDBS supports PMR because of its performance characteristics for query processing (due to a linear sort/join algorithm) and because intermediate query results are stored as “first class” relations. Thus the materialization required by PMR is directly supported. This paper introduces PMR and the USL MMDBS and presents preliminary performance results.

The effects of relational and entity-relationship data models on query performance of end users

In database systems the end user interacts with the database at the external schema level. At this level the user sees only the logical structure of the database that is relevant to his/her work. Both the relational and the entity-relationship model have proponents arguing that one data model is superior to the other when used in the end user environment. However, a literature review indicated that these arguments have not been based on empirical results from a systematic inquiry. The study reported here examined this issue through a controlled experiment using query writing as the task. Our basic assumption was that if one data model was superior to the other, then the superiority of the model would be reflected in the user's query writing performance. In addition, this superiority would be demonstrated on both simple and complex tasks. Query writing performance was measured by three variables: number of syntax errors, number of semantic errors, and amount of time to complete queries. The results indicated that subjects using the relational model made fewer syntax errors, but required more time to complete a query. No significant differences in the number of semantic errors were found between the two data models. Based on these results, neither the relational nor the entity-relational data model was clearly superior when used as the interface between a database system and the end user. As expected, the more complex tasks caused more syntax and semantic errors, and required more time to complete.

An average-case analysis of MAT and inverted file

It is shown in the literature that the multiple attribute tree outperforms inverted files in terms of worst-case complexities for partial-match queries. In this paper, we estimate the expected values for the complexities of both complete match query and range query on multiple attribute tree and inverted files. We use a uniform probabilistic model for the input data space. We show that the multiple attribute tree is more efficient than the inverted file in terms of these expected value measures.

A sound and complete query evaluation algorithm for relational databases with null values

Reiter has proposed extended relational theory to formulate relational databases with null values and presented a query evaluation algorithm for such databases. However, due to indefinite information brought in by null values, Reiter's algorithm is sound but not complete. In this paper, we first propose an extended relation to represent indefinite information in relational databases. Then, we define an extended relational algebra for extended relations. Based on Reiter's extended relational theory, and our extended relations and the extended relational algebra, we present a sound and complete query evaluation algorithm for relational databases with null values

A sound and sometimes complete query evaluation algorithm for relational databases with null values

A sound and, in certain cases, complete method is described for evaluating queries in relational databases with null values where these nulls represent existing but unknown individuals. The soundness and completeness results are proved relative to a formalization of such databases as suitable theories of first-order logic. Because the algorithm conforms to the relational algebra, it may easily be incorporated into existing relational systems.

Specification of a query language by the attribute method

Data base applications are large and complex in nature. Therefore we need effective tools to manage complexity in developing data base software. In this paper we demonstrate how the attribute method can be used as a systematic approach to specify software in the data area. We have a relationally complete query language as the specification subject. In data base applications it is important to specify precisely both the semantics of software and constraints related to data base structures and to data base operations. For the former purpose we definesemantic attributes and for the latterchecking attributes.