Conceptual Modeling and Querying inMultimedia Databases

Given a very large multimedia database, how to process k-nearest-neighbor queries efficiently? While the sequential scan is one of the most obvious solutions for small-to-moderate multimedia databases, it becomes practically infeasible when the database size grows. Concomitant with the volume and velocity of data, multimedia databases are frequently endowed with a complex distance-based similarity model that supports content-based data access in an adjustable and adaptive manner. Typical for many state-of-the-art distance-based similarity models is an at least quadratic computation time complexity for a single distance evaluation between two multimedia objects. Thus the search for the most query-like multimedia objects is still one of the major challenges.In this paper, we address the problem of optimal k-nearest-neighbor query processing via multiple lower bound approximations in very large multimedia databases. To this end, we propose the concepts of filter-optimality and refinement-optimality and present the Cascading Multi-Step Algorithm and the Interleaved Multi-Step Algorithm for fast query processing. Besides the algorithms’ properties, we study their query processing performance with respect to the number of CPU and I/O operations on large-scale benchmark multimedia databases. Our performance analysis shows how to process k-nearest-neighbor queries in multimedia databases efficiently and provides a guide for further research.

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.

Data mining is the discovery of knowledge and useful information from the large amounts of data stored in databases. The emerging data mining tools and systems lead to the demand of a powerful data mining query language. The concepts of such a language for relational databases are discussed before. With the increasing popularity of object-oriented databases, it is important to design a data mining query language for such databases. The main objective of this paper is to propose an Object Data Mining Query Language (ODMQL) for object-oriented databases as an extension to the Object Query Language (OQL) proposed by the Object Data Management Group (ODMG) as a standard query language for object-oriented databases. The proposed language is implemented as a feature of an experimental object-oriented database management system that is developed as a testbed for research issues of object-oriented databases.

This reports on an investigation focused on a transform mechanism for a natural language database query system being undertaken at the University of Cincinnati. The mechanism studies the questions involved in a natural language interface between the human environment and the database query languages used to access machine stored data. A major product of the research would be a methodology for the transformation of database queries in a natural language into database queries stated in the data manipulation language of a DBMS.

Database technology is evolving to accommodate complex multimedia objects — data types that present unique challenges in the storage and movement of large time-dependent objects. The manipulation and analysis of these large and semantically rich objects require techniques far different from those used for alphanumeric databases. This paper presents multimedia server-based architectures based on massively parallel processor systems and an object-relational database management system. The transition from client-centric multimedia applications to a client/server environment is examined, and several applications are discussed that illustrate the use of multimedia database servers based on the object-relational model. Finally, this paper reviews the architecture of two scaleable multimedia servers that can support these applications: MoonBase, a database server with full content analysis capability for multimedia objects, and the audio/video server, a system-based file server that can operate within the MoonBase architecture.

Semi-Formal Query Language Interface (SFQI) is a user interface for interaction with database systems. SFQI is based on a Semi-Formal Query Language (SFQL) which is an enhancement of SQL. SFQI provides facilities for guiding users in performing database querying, such as formulating a valid database query, refining user-misconceptions and modifying database query misconstructions. The syntax of SFQL is not as rigid as in most database query languages. The system provides logical database navigation and users are not required to know, in detail, the structure of the database being queried.

Query languages for graph databases started to be investigated some 25 years ago. With much current data, such as linked data on the Web and social network data, being graph-structured, there has been a recent resurgence in interest in graph query languages. We provide a brief survey of many of the graph query languages that have been proposed, focussing on the core functionality provided in these languages. We also consider issues such as expressive power and the computational complexity of query evaluation.

Graph analytics is becoming increasingly popular, driving many important business applications from social network analysis to machine learning. Since most graph data is collected in a relational database, it seems natural to attempt to perform graph analytics within the relational environment. However, SQL, the query language for relational databases, makes it difficult to express graph analytics operations. This is because SQL requires programmers to think in terms of tables and joins, rather than the more natural representation of graphs as collections of nodes and edges. As a result, even relatively simple graph operations can require very complex SQL queries. In this paper, we present GRAPHiQL, an intuitive query language for graph analytics, which allows developers to reason in terms of nodes and edges. GRAPHiQL provides key graph constructs such as looping, recursion, and neighborhood operations. At runtime, GRAPHiQL compiles graph programs into efficient SQL queries that can run on any relational database. We demonstrate the applicability of GRAPHiQL on several applications and compare the performance of GRAPHiQL queries with those of Apache Giraph (a popular ‘vertex centric’ graph programming language).

Patterns describe common solutions to recurring problems. This paper presents a pattern-based approach of improving database design and automating generation of statements that are written in a database language. During the logical design of a database, a database designer has to find domain-independent database design patterns that are applicable in the database. The selected patterns are used in order to generate code during implementation of the database. For example, this approach can be used in order to generate implementations of integrity constraints and updatable views. We present a pattern-based and template-based code generator that is created specifically for the development of databases.

Multimedia data mining refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. Two typical tasks in multimedia data mining are of visual data classification and clustering in terms of semantics. Usually performance of such classification or clustering systems may not be favorable due to the use of low-level features for image representation, and also some improper similarity metrics for measuring the closeness between multimedia objects as well. This paper considers a problem of modeling similarity for semantic image clustering. A collection of semantic images and feed-forward neural networks are used to approximate a characteristic function of equivalence classes, which is termed as a learning pseudo metric (LPM). Empirical criteria on evaluating the goodness of the LPM are established. A LPM based k-Mean rule is then employed for the semantic image clustering practice, where two impurity indices, classification performance and robustness are used for performance evaluation. An artificial image database with 11 semantics is employed for our simulation studies. Results demonstrate the merits and usefulness of our proposed techniques for multimedia data mining.

The theme of the MDMKDD'13 workshop is\mining large scale rich content in a networked society." The workshop brought together experts in computational analysis on digital media content and multimedia databases, as well as knowledge engineers and domain experts from different applied disciplines with potential in multimedia data mining. The new theme of this year's workshop highlights the integration of multimedia in people's daily lives. With the rapid growth of mobile devices and personalized data, we wanted the workshop to focus on mining large scale rich content which is abundant in today's networked societies. Vast amount of multimedia data are produced, shared, and accessed everyday in various social platforms. These multimedia objects (images, videos, texts, tags, etc.) represent rich, multifaceted recordings of human behavior in the networked society, which lead to a range of social applications such as consumer behavior forecasting, social driven advertising / business, local knowledge discovery (e.g., for tourism or shopping), detection of emergent news events and trends, and so on. In addition to techniques for mining single media items, all of these applications require new methods for extracting robust features and discovering stable relationships among different media modalities and the users, in a dynamic, social context rich, and likely noisy environment. Multimedia is designed to stimulate the human senses beyond text. Using the rich content in multiple media types, we can capture the full experience of an event. Mining the data within media rich platforms and immersive environments (such as online communities, blogs, social networks, and virtual worlds) integrates the digital and physical experiences and information. The aim of the MDMKDD'13 workshop is to explore the role that multimedia data mining can play to enhance virtual society experiences.

An object-relational database is the entry point for geoinformatic exploration tools applied for mineral targeting. A data model is proposed to establish standards for database contents that enable information sharing across multidisciplinary fields, modeling geological processes a database, and to provid data for advanced modeling tasks. An application-specific database model for mineral targeting was based largely on the generic North American Data Model Conceptual Design 1.0 and the ESRI Geology Data Model 812. These data models were modified and extended to datasets typically involved within research and exploration of mineral deposits. The development of a conceptual model into physical database was achieved through the integration of a relational database and a geographic information system. The result is a highly visual relational database in which database query, analysis, and visualization of subsurface geologic data can be performed visually on computer screen via a feature interface. The case study shows that navigation and access to the contents of database can be done effectively through relational query protocols. Results of queries become input data for advanced modeling tasks such as geostatistical analysis, solid 3D modeling, and hydrothermal system modeling.

This article will highlight the framework opted by the authors in developing a database query system for usage on mobile phones. As the development work is still in progress, the authors will share some of the approaches taken in developing a prototype for a relationally complete database query language. This work concentrates on developing an application-independent, relationally complete database query language. The remainder of this article is organized as follows. The next section presents some of the existing work related to the study. We then introduce and describe the framework undertaken in order to develop a database query system for mobile phones, and discuss the prototype of the database query language used by the system. We end with our conclusion.

We study adding aggregate operators, such as summing up elements of a column of a relation, to logics with counting mechanisms. The primary motivation comes from database applications, where aggregate operators are present in all real life query languages. Unlike other features of query languages, aggregates are not adequately captured by the existing logical formalisms. Consequently, all previous approaches to analyzing the expressive power of aggregation were only capable of producing partial results, depending on the allowed class of aggregate and arithmetic operations. We consider a powerful counting logic, and extend it with the set of all aggregate operators. We show that the resulting logic satisfies analogs of Hanf's and Gaifman's theorems, meaning that it can only express local properties. We consider a database query language that expresses all the standard aggregates found in commercial query languages, and show how it can be translated into the aggregate logic, thereby providing a number of expressivity bounds, that do not depend on a particular class of arithmetic functions, and that subsume all those previously known. We consider a restricted aggregate logic that gives us a tighter capture of database languages, end also use it to show that some questions on expressivity of aggregation cannot be answered without resolving some deep problems in complexity theory.

In this paper we describe a methodology that we have developed to teach multimedia database design. Multimedia databases have novel requirements compared with conventional text and numeric databases, in particular the need to consider the most appropriate physical formats of the digital artefacts and the inherent polysemy of media artefacts. However the need to be able to model a problem at a high level of abstraction, and to proceed from a conceptual to physical solution, remain essential. The approach we describe here ensures a parallel focus on representing both the role of the media artefact in the context of the database, and its appropriate technical specification, and incorporates media requirements into a conventional data modelling technique. The database design and documentation produced allow considerations of specific implementation to be deferred as long as possible.