Querying Multimedia Data by Similarity in Relational DBMS

Multimedia objects – such as images, audio, and video – do not present the total ordering relationship, so the relational operators (‘<’, ‘=’, ‘=’, ‘>’) are not suitable to compare them. Therefore, similarity queries are the most useful, and often the only types of queries adequate to search multimedia objects stored in a database. Unfortunately, the ubiquitous query language SQL – the most widely employed language in Database Management Systems (DBMS) – does not provide effective support for similarity queries. This chapter presents an already validated strategy that adds similarity queries to SQL, supporting a powerful set of similarity operators. The chapter also describes techniques to store and retrieve multimedia objects in an efficient way and shows existing DBMS alternatives to execute similarity queries over multimedia data.

Flexible Querying of Imperfect Temporal Metadata in Spatial Data Infrastructures

Spatial Data Infrastructures (SDI) allow users connected to the Internet to share and access remote and distributed heterogeneous geodata that are managed by their providers at their own Web sites. In SDIs, available geodata can be found via standard discovery geo-services that makes available query facilities of a metadata catalog. By expressing precise selection conditions on the values of the metadata collected in the catalog, the user can discover interesting and relevant geodata and then access them by means of the services of the SDI. An important dimension of geodata that often concerns such users’ requests is the temporal information that can have multiple semantics. Current practice to perform geodata discovery in SDIs is inadequate for several reasons. First of all, with respect to the temporal characterization, available recommendations for metadata specification, for example, the INSPIRE Directive of the European community do not consider the multiple semantics of the temporal metadata. To this aim, this chapter proposes to enrich the current temporal metadata with the possibility to indicate temporal metadata related to both the observations, i.e., the geodata, the observed event, i.e., the objects in the geodata, and the temporal resolution of observations, i.e., their timestamps. The chapter introduces also a proposal to manage temporal series of geodata observed at different dates. Moreover, in order to represent the uncertain and incomplete knowledge of the time information on the available geodata, the chapter proposes a representation for imperfect temporal metadata within the fuzzy set framework. Another issue that is faced in this chapter is the inadequacy of current discovery service query facilities: in order to obtain a list of geodata results, corresponding values of metadata must exactly match the query conditions. To allow more flexibility, the chapter proposes to adopt the framework of fuzzy databases to allow expressing soft selection conditions, i.e., tolerant to under-satisfaction, so as to retrieve geodata in decreasing order of relevance to the user needs. The chapter illustrates this proposal by an example.

Remarks on a Fuzzy Approach to Flexible Database Querying, Its Extension and Relation to Data Mining and Summarization

For an effective and efficient information search of databases, various issues should be solved. A very important one, though still usually neglected by traditional database management systems, is related to a proper representation of user preferences and intentions, and then their representation in querying languages. In many scenarios, they are not clear-cut, and often have their original form deeply rooted in natural language implying a need of flexible querying. Although the research on introducing elements of natural language into the database querying languages dates back to the late 1970s, the practical commercial solutions are still not widely available. This chapter is meant to revive the line of research in flexible querying languages based on the use of fuzzy logic. This chapter recalls details of a basic technique of flexible fuzzy querying, discusses some newest developments in this area and, moreover, shows how other relevant tasks may be implemented in the framework of such queries interface. In particular, it considers fuzzy queries with linguistic quantifiers and shows their intrinsic relation with linguistic data summarization. Moreover, the chapter mentions so called “bipolar queries” and advocates them as a next relevant breakthrough in flexible querying based on fuzzy logic and possibility theory.

Concept-Oriented Query Language for Data Modeling and Analysis

This chapter describes a novel query language, called the concept-oriented query language (COQL), and demonstrates how it can be used for data modeling and analysis. The query language is based on a novel construct, called concept, and two relations between concepts, inclusion and partial order. Concepts generalize conventional classes and are used for describing domain-specific identities. Inclusion relation generalizes inheritance and is used for describing hierarchical address spaces. Partial order among concepts is used to define two main operations: projection and de-projection. This chapter demonstrates how these constructs are used to solve typical tasks in data modeling and analysis such as logical navigation, multidimensional analysis, and inference.

Automatic Categorization of Web Database Query Results

Web database queries are often exploratory. The users often find that their queries return too many answers and many of them may be irrelevant. Based on different kinds of user preferences, this chapter proposes a novel categorization approach which consists of two steps. The first step analyzes query history of all users in the system offline and generates a set of clusters over the tuples, where each cluster represents one type of user preference. When a user issues a query, the second step presents to the user a category tree over the clusters generated in the first step such that the user can easily select the subset of query results matching his needs. The problem of constructing a category tree is a cost optimization problem and heuristic algorithms were developed to compute the min-cost categorization. The efficiency and effectiveness of our approach are demonstrated by experimental results.

Making Query Coding in SQL Easier by Implementing the SQL Divide Keyword

Relational Algebra (RA) and structured query language (SQL) are supposed to have a bijective relationship by having the same expressive power. That is, each operation in SQL can be mapped to one RA equivalent and vice versa. Actually, this is an essential fact because in commercial database management systems, every SQL query is translated into equivalent RA expression, which is optimized and executed to produce the required output. However, RA has an explicit relational division symbol (÷), whereas SQL does not have a corresponding explicit division keyword. Division is implemented using a combination of four core operations, namely cross product, difference, selection, and projection. In fact, to implement relational division in SQL requires convoluted queries with multiple nested select statements and set operations. Explicit division in relational algebra is possible when the divisor is static; however, a dynamic divisor forces the coding of the query to follow the explicit expression using the four core operators. On the other hand, SQL does not provide any flexibility for expressing division when the divisor is static. Thus, the work described in this chapter is intended to provide SQL expression equivalent to explicit relational algebra division (with static divisor). In other words, the goal is to implement a SQL query rewriter in Java which takes as input a divide grammar and rewrites it to an efficient query using current SQL keywords. The developed approach could be adapted as front-end or wrapper to existing SQL query system.Users will be able to express explicit division in SQL which will be translated into an equivalent expression that involves only the standard SQL keywords and structure. This will turn SQL into more attractive for specifying queries involving explicit division.

An Extended Relational Model & SQL for Fuzzy Multidatabases

Many real world problems involve imprecise and ambiguous information rather than crisp information. Recent trends in the database paradigm are to incorporate fuzzy sets to tackle imprecise and ambiguous information of real world problems. Fuzzy query processing in multidatabases have been extensively studied, however, the same has rarely been addressed for fuzzy multidatabases. This chapter attempts to extend the SQL to formulate a global fuzzy query on a fuzzy multidatabase under FTS relational model discussed earlier. The chapter provides architecture for distributed fuzzy query processing with a strategy for fuzzy query decomposition and optimization. Proofs of consistent global fuzzy operations and some of algebraic properties of FTS Relational Model are also supplemented.

Fuzzy Querying Capability at Core of a RDBMS

This chapter is focused in incorporating the fuzzy capabilities to a relational database management system (RDBMS) of open source. The fuzzy capabilities include connectors, modifiers, comparators, quantifiers, and queries. The extensions consider a more flexible DDL and DML languages. The aim is to show the design and implementation details in the RDBMS PostgreSQL. For this, a fuzzy query processor and fuzzy access mechanism has been designed and implemented. The physical fuzzy relational operators have been also defined and implemented. The flow of a fuzzy query through the different modules (parser, planner, optimizer, and executor) has been shown. Some experimental results have been included to demonstrate the performance of the proposal solution. These results show that the extensions have not decreased the performance of the RDBMS.

Evaluating Top-k Skyline Queries Efficiently

Criteria that induce a Skyline naturally represent user’s preference conditions useful to discard irrelevant data in large datasets. However, in the presence of high-dimensional Skyline spaces, the size of the Skyline can still be very large. To identify the best k points among the Skyline, the Top-k Skyline approach has been proposed. This chapter describes existing solutions and proposes to use the TKSI algorithm for the Top-k Skyline problem. TKSI reduces the search space by computing only a subset of the Skyline that is required to produce the top-k objects. In addition, the Skyline Frequency Metric is implemented to discriminate among the Skyline objects those that best meet the multidimensional criteria. This chapter’s authors have empirically studied the quality of TKSI, and their experimental results show the TKSI may be able to speed up the computation of the Top-k Skyline in at least 50% percent with regard to the state-of-the-art solutions.

Querying Graph Databases

Recently, there has been a lot of interest in the application of graphs in different domains. Graphs have been widely used for data modeling in different application domains such as: chemical compounds, protein networks, social networks, and Semantic Web. Given a query graph, the task of retrieving related graphs as a result of the query from a large graph database is a key issue in any graph-based application. This has raised a crucial need for efficient graph indexing and querying techniques. This chapter provides an overview of different techniques for indexing and querying graph databases. An overview of several proposals of graph query language is also given. Finally, the chapter provides a set of guidelines for future research directions.