RAM: array processing over a relational DBMS

Developing multimedia applications in relational databases is hindered by a mismatch in computational frameworks. Efficient manipulation of multimedia data calls for array-based processing, which at best is available as a database add-on, not supported by the query optimizer. As a result, array-based processing ends up in dedicated programs outside the DBMS: non-reusable black boxes. The goal of our research is to reduce this gap between user-needs and system functionality by developing a seemless integration of array processing in a relational algebra engine. The paper introduces a declarative language for array-expressions based on the array comprehension, and its mapping to a relational kernel in a prototype implementation. The layered architecture of the resulting array database management system allows the use of structural knowledge available in the array data type. This additional source of information can be exploited for query optimization, which is demonstrated with a case study. The experiments show how the performance of a standard tool for matrix computations can be achieved without sacrificing data independence, highlighting however a critical aspect in the DBMS architecture proposed

Automatic optimization of array queries

Non-trivial scientific applications often involve complex computations on large multi-dimensional datasets. Using relational database technology for these datasets is cumbersome since expressing the computations in terms of relational queries is difficult and time-consuming. Moreover, query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these issues by providing a transparent mapping between the scientific problem specification and the underlying database system. This paper focuses on the RAM query optimizer which is specifically tuned to exploit the characteristics of the array paradigm. We detail how an intermediate array-algebra and several equivalence rules are used to create efficient query plans and how, with minor extensions, the optimizer can automatically parallelize array operation

A Case Study on Array Query Optimisation

The development of applications involving multi-dimensional data sets on top of a RDBMS raises several difficulties that are not directly related to the scientific problem being addressed. In particular, an additional effort is needed to solve the mismatch existing between the array-based data model typical for such computations and the set-based data model provided by the RDMBS. The RAM (Relational Array Mapping) system fills this gap, silently providing a mapping layer between the two data models. As expected though, a naive implementation of such an automatic translation cannot compete with the efficiency of queries written by an experienced programmer. In order to make RAM a valid alternative to expensive and time-consuming hand-written solutions, this performance gap should be reduced. We study a real-world application aimed at the ranking of multimedia collections to assess the impact of different implementation strategies. The result of this study provides an illustrative outlook for the development of generally applicable optimisation techniques

Virtual context - relating media objects to their real world subjects

Virtual Reality (VR) is sometimes used to give the user an immersive, three-dimensional sense of a real-world setting. VR is also sometimes used for information visualization, taking advantage of the perceptual characteristics of VR to convey information. This paper presents the Dam Square Virtual Context, a Web-based VR that is a combination of both these uses. This VR presents mock-ups of both Dam Square in the city of Amsterdam and the city's largest museum, the Rijksmuseum. This VR conveys abstract information that includes the relationships between objects in the museum, such as paintings of Amsterdam cityscapes, and the corresponding objects in Dam Square itself, such as the buildings and neighborhoods shown in these paintings. It is thus a multimedia combining VR wireframes and bitmaps with still images and interface scripts. The principle behind this is that the user learns how to walk through the museum to view objects that together convey a unified view about Amsterdam, which the user can then later walk through

Distribution Rules for Array Database Queries

Non-trivial retrieval applications involve complex computations on large multi-dimensional datasets. These should, in principle, benefit from the use of relational database technology. However, expressing such problems in terms of relational queries is difficult and timeconsuming. Even more discouraging is the efficiency issue: query optimization strategies successful in classical relational domains may not suffice when applied to the multi-dimensional array domain. The RAM (Relational Array Mapping) system hides these difficulties by providing a transparent mapping between the scientific problem specification and the underlying database system. In addition, its optimizer is specifically tuned to exploit the characteristics of the array paradigm and to allow for automatic balanced work-load distribution. Using an example taken from the multimedia domain, this paper shows how a distributed realword application can be efficiently implemented, using the RAM system, without user intervention

Known-item retrieval on broadcast TV

Many content-based, multimedia retrieval systems on a feature-oriented approach querying, mostly exposing a features (introduced design time) querying purposes. This restriction a limited of features problematic reasons: it restricts expressiveness semantic seems unfeasible obtain (a-priori) su#ciently powerful of features possible queries. describe an alternative approach where users specify precisely distinguishable characteristics desired result In this process, user describes representation content (based a feature collection features) then system apply representation search. prototype video retrieval system allows expression of such queries a sequence of operations, MPEG video audio streams, executed database system. While low-level decompression stage implemented in an imperative programming language, actual retrieval approach is expressed in declarative database queries. assessed system study known-item retrieval on broadcast streams: detecting news bulletins in stream, with of both audio video information. Mathematics Subject Classification: 68P20, 68U99 Computing Classification System: Information Search Retrieval (H.3.3), Multimedia Infor- mation Systems (H.5.1) Keywords Phrases: multimedia retrieval systems, multimedia database systems, known-item retrieval Note: carried under project DRUID project report been submitted article to 1

CWI at the TREC-2002 video track

We present a probabilistic model for the retrieval of multimodal documents. The model is based on Bayesian decision theory and combines models for text based search with models for visual search. The textual model, applied to the LIMSI transcripts, is based on the language modelling approach to text retrieval. The visual model, a mixture of Gaussian densities, describes keyframes selected from shots. Both models have proved successful on media specific retrieval tasks. Our contribution is the combination of both techniques in a unified model, ranking shots on ASR-data and visual features simultaneously