Gio Wiederhold

Mediators in the architecture of future information systems

For single databases, primary hindrances for end-user access are the volume of data that is becoming available, the lack of abstraction, and the need to understand the representation of the data. W...For single databases, primary hindrances for end-user access are the volume of data that is becoming available, the lack of abstraction, and the need to understand the representation of the data. When information is combined from multiple databases, the major concern is the mismatch encountered in information representation and structure. Intelligent and active use of information requires a class of software modules that mediate between the workstation applications and the databases. It is shown that mediation simplifies, abstracts, reduces, merges, and explains data. A mediator is a software module that exploits encoded knowledge about certain sets or subsets of data to create information for a higher layer of applications. A model of information processing and information system components is described. The mediator architecture, including mediator interfaces, sharing of mediator modules, distribution of mediators, and triggers for knowledge maintenance, are discussed.<<ETX>>

SIMPLIcity: semantics-sensitive integrated matching for picture libraries

We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.

Vertical partitioning algorithms for database design

This paper addresses the vertical partitioning of a set of logical records or a relation into fragments. The rationale behind vertical partitioning is to produce fragments, groups of attribute columns, that “closely match” the requirements of transactions. Vertical partitioning is applied in three contexts: a database stored on devices of a single type, a database stored in different memory levels, and a distributed database. In a two-level memory hierarchy, most transactions should be processed using the fragments in primary memory. In distributed databases, fragment allocation should maximize the amount of local transaction processing. Fragments may be nonoverlapping or overlapping. A two-phase approach for the determination of fragments is proposed; in the first phase, the design is driven by empirical objective functions which do not require specific cost information. The second phase performs cost optimization by incorporating the knowledge of a specific application environment. The algorithms presented in this paper have been implemented, and examples of their actual use are shown.

IRM: integrated region matching for image retrieval

Content-based image retrieval using region segmentation has been an active research area. We present IRM (Integrated Region Matching), a novel similarity measure for region-based image similarity comparison. The targeted image retrieval systems represent an image by a set of regions, roughly corresponding to objects, which are characterized by features reflecting color, texture, shape, and location properties. The IRM measure for evaluating overall similarity between images incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, the overall similarity approach reduces the influence of inaccurate segmentation, helps to clarify the semantics of a particular region, and enables a simple querying interface for region-based image retrieval systems. The IRM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The application to a database of about 200,000 general-purpose images shows exceptional robustness to image alterations such as intensity variation, sharpness variation, color distortions, shape distortions, cropping, shifting, and rotation. Compared with several existing systems, our system in general achieves more accurate retrieval at higher speed.

The conceptual basis for mediation services

As information systems grow, they depend increasingly on diverse, heterogeneous resources, such as databases, knowledge bases, bibliographic files, Web-based information, computational facilities, digital libraries, geographic information systems, and simulations. Users typically develop and maintain these resources autonomously. Mediator modules comprise a layer of intelligent middleware services in information systems that link data resources and application programs. Currently, system developers must build intelligent mediators by carefully acquiring domain knowledge and handcrafting the required code. This article presents the conceptual underpinning for automating mediation.

Read-only transactions in a distributed database

A read-only transaction or query is a transaction which does not modify any data. Read-only transactions could be processed with general transaction processing algorithms, but in many cases it is more efficient to process read-only transactions with special algorithms which take advantage of the knowledge that the transaction only reads. This paper defines the various consistency and currency requirements that read-only transactions may have. The processing of the different classes of read-only transactions in a distributed database is discussed. The concept of R insularity is introduced to characterize both the read-only and update algorithms. Several simple update and read-only transaction processing algorithms are presented to illustrate how the query requirements and the update algorithms affect the read-only transaction processing algorithms.

Incremental recomputation of active relational expressions

Database updates are small and incremental compared to database contents. It is therefore desirable that recomputations of active relational expressions-such as views, derived data, integrity constraints, active queries, and monitors-can also be performed incrementally. An efficient algorithm for the incremental recomputation of active relational expressions based on finite differencing techniques is presented. Database updates are modeled as incremental changes to database relations, and the algorithm derives, by update propagation, the minimal incremental relational expressions that need recomputation. The algorithm has applications in the maintenance of materialized views and derived data, the checking of integrity constraints, and the evaluation of active queries and monitors. >

Toward megaprogramming

Megaprogramming is a technology for programming with large modules called megamodules that capture the functionality of services provided by large organizations like banks, airline reservation systems, and city transportation systems. Megamodules are internally homogeneous, independently maintained software systems managed by a community with its own terminology, goals, knowledge, and programming traditions. Each megamodule describes its externally accessible data structures and operations and has an internally consistent behavior. The concepts, terminology, and interpretation paradigm of a megamodule is called its ontology.

Unsupervised multiresolution segmentation for images with low depth of field

Unsupervised segmentation of images with low depth of field (DOF) is highly useful in various applications. This paper describes a novel multiresolution image segmentation algorithm for low DOF images. The algorithm is designed to separate a sharply focused object-of-interest from other foreground or background objects. The algorithm is fully automatic in that all parameters are image independent. A multi-scale approach based on high frequency wavelet coefficients and their statistics is used to perform context-dependent classification of individual blocks of the image. Unlike other edge-based approaches, our algorithm does not rely on the process of connecting object boundaries. The algorithm has achieved high accuracy when tested on more than 100 low DOF images, many with inhomogeneous foreground or background distractions. Compared with he state of the art algorithms, this new algorithm provides better accuracy at higher speed.

Clustering for approximate similarity search in high-dimensional spaces

We present a clustering and indexing paradigm (called Clindex) for high-dimensional search spaces. The scheme is designed for approximate similarity searches, where one would like to find many of the data points near a target point, but where one can tolerate missing a few near points. For such searches, our scheme can find near points with high recall in very few IOs and perform significantly better than other approaches. Our scheme is based on finding clusters and, then, building a simple but efficient index for them. We analyze the trade-offs involved in clustering and building such an index structure, and present extensive experimental results.