Visualization of path expressions in a visual object-oriented database query language

VOQL*: A Visual Object Query Language With Inductively Defined Formal Semantics

Text based queries often lead tend to be complex, and may result in non user friendly query structures. However, querying information systems using visual means, even for complex queries has proven to be more efficient and effective as compared to text based queries. This is owing to the fact that visual systems make way for better human-computer communication. This paper introduces an improved query system using a Visual Query Language. The system allows the users to construct query graphs by interacting with the ontology in a user friendly manner. The main purpose of the system is to enable efficient querying on ontologies even by novice users who do not have an in-depth knowledge of internal query structures. The system also supports graphical recursive queries and methods to interpret recursive programs from these visual query graphs. Additionally, we have performed some preliminary usability experiments to test the efficiency and effectiveness of the system.

Multimedia data are now available to a variety of users ranging from naive to sophisticated. To make querying easy, visual query languages have been proposed. Most of these languages have a low expressive power and have their own query processors. Efforts have been made to design query languages with proper semantics to facilitate query optimization and processing in existing database systems. The majority of multimedia database systems are built on top of object or object-relational database systems with the underlying query facilities inherited. The DISIMA system is being built on top of a commercial OODBMS and we have chosen to extend the standard object oriented query language OQL with some multimedia functionalities. The resulting language is called MOQL. This paper presents VisualMOQL, a visual query language implementing the image component of MOQL.

Consumer photos exhibit highly varied contents, diverse resolutions and inconsistent quality. The objects are usually ill-posed, occluded, and cluttered with poor lighting, focus, and exposure. Existing image retrieval approaches face many obstacles such as robust object segmentation, small sampling problem during relevance feedback, semantic gap between low-level features and high-level semantics, etc.We propose a structured learning approach to design domain-relevant visual semantics, known as semantic support regions, to support semantic indexing and visual query for consumer photos. Semantic support regions are segmentation-free image regions that exhibit semantic meanings and that can be learned statistically to span a new indexing space. They are detected from image content, reconciled across multiple resolutions, and aggregated spatially to form local semantic histograms.Query by Spatial Icons (QBSI) is a unique visual query language to specify semantic icons and spatial extents in a Boolean expression. Based on 2400 heterogeneous consumer photos and 26 semantic support regions learned from a small training set, we demonstrate the usefulness of the visual query language with 15 QBSI queries that have attained high precision values at top retrieved images.

For multimedia databases, there are not only different media types, but also different ways to query the databases. The query mechanisms may include free text search, SQL-like querying, icon-based techniques, querying based upon the entity-relationship (ER) diagram, content-based querying, sound-based querying, as well as virtual-reality (VR) techniques. We will first discuss query languages for multimedia databases with emphasis on visual query languages, and then survey recent advances in query languages for heterogeneous multimedia databases and data sources. A powerful spatial/temporal query language called ΣQL is described, and we will illustrate this approach by data fusion examples.

The visual query language CQL for transitive and relational computation

We define a graph based visual query language for object databases, called QUIVER. Most graph based query languages proposed in the literature view queries either as constraint (pattern) graphs or as data flow graphs. QUIVER, however allows constraints and data flow to be combined in queries. This combination of constraints and data flow is motivated by the need to support comprehensive object models, such as that proposed by the Object Data Management Group (ODMG). QUIVER supports all ODMG object model constructs, including computational constructs such as operations (methods). In addition, QUIVER supports computational query constructs such as subqueries and (aggregate) functions.

In this paper we introduce and formally define Query by Browsing (QBB), a scalable, relationally complete visual query language based on the desktop user interface paradigm and tuple relational calculus that allows the formulation of complex queries over relational, entity-relationship, object-oriented and XML data sources on a variety of handheld and desktop platforms. It is to our knowledge the first visual query language to combine the important characteristics of usability, scalability, expressive power and flexibility. We support these claims by demonstrating the similarity of the QBB paradigm to the popular desktop user interface paradigm, by relating it to relational calculus and relational algebra and by describing Chiromancer II, a Web-based implementation of the QBB paradigm for handheld devices. We also discuss ways in which non-relational sources can be represented and queried and compare QBB to related work in the area of visual query languages for a variety of data models. We finally offer conclusions and thoughts for future work.

We present a first treatment dealing with semantics of visual spatial query languages for GIS using a suitable description logic. This decidable space logic is described and its usefulness for GIS exemplified. The logic supports the specification of semantics, reasoning about query subsumption and about applying default knowledge.

In this paper we introduce DOODLE, a new visual and declarative language for object-oriented databases. The main principle behind the language is that it is possible to display and query the database with arbitrary pictures . We allow the user to tailor the display of the data to suit the application at hand or her preferences. We want the user-defined visualizations to be stored in the database, and the language to express all kinds of visual manipulations. For extendibility reasons, the language is object-oriented. The semantics of the language is given by a well-known deductive query language for object-oriented databases. We hope that the formal basis of our language will contribute to the theoretical study of database visualizations and visual query languages, a subject that we believe is of great interest, but largely left unexplored.

Applicability of genetic algorithms to optimal evaluation of path predicates in object-oriented queries

We define a standard of effectiveness for a database calculus relative to a query language. Effectiveness judges suitability to serve as a processing framework for the query language, and comprises aspects of coverage, manipulability and efficient evaluation. We present the monoid calculus, and argue its effectiveness for object-oriented query languages, exemplified by OQL of ODMG-93. The monoid calculus readily captures such features as multiple collection types, aggregations, arbitrary composition of type constructors and nested query expressions. We also show how to extend the monoid calculus to deal with vectors and arrays in more expressive ways than current query languages do, and illustrate how it can handle identity and updates.

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.

A new kind of data model has recently emerged in which the database is not constrained by a conventional schema. Systems like ACeDB, which has become very popular with biologists, and the recent Tsimmis proposal for data integration organize data in tree-like structures whose components can be used equally well to represent sets and tuples. Such structures allow great flexibility y in data representation.What query language is appropriate for such structures? Here we propose a simple language UnQL for querying data organized as a rooted, edge-labeled graph. In this model, relational data may be represented as fixed-depth trees, and on such trees UnQL is equivalent to the relational algebra. The novelty of UnQL consists in its programming constructs for arbitrarily deep data and for cyclic structures. While strictly more powerful than query languages with path expressions like XSQL, UnQL can still be efficiently evaluated. We describe new optimization techniques for the deep or vertical dimension of UnQL queries. Furthermore, we show that known optimization techniques for operators on flat relations apply to the horizontal dimension of UnQL.

Much current research on digital libraries focuses on named entity extraction and transformation into structured information. Examples include entities like events, people, and places, and attributes like birth date or latitude. This video demonstration illustrates the potential for finding relationships among entities extracted from 50,000 news segments from CMUs Informedia Digital Video Library. A visual query language is used to specify relationships among entities. Data populate the query structure, which becomes an interface for exploration that gives continuous feedback in the form of visualizations of summary statistics. The target user is a data analyst familiar with the domain from which the entities come, but not a computer scientist.