Special issue: Selected papers from the sixth IFIP 2.6 working conference on visual database systems: Guest editors' introduction

While multimedia data, image data in particular, is an integral part of most websites and web documents, our quest for information so far is still restricted to text based search. To explore the World Wide Web more effectively, especially its rich repository of truly multimedia information, we are facing a number of challenging problems. Firstly, we face the ambiguous and highly subjective nature of defining image semantics and similarity. Secondly, multimedia data could come from highly diversified sources, as a result of automatic image capturing and generation processes. Finally, multimedia information exists in decentralised sources over the Web, making it difficult to use conventional content-based image retrieval (CBIR) techniques for effective and efficient search. In this special issue, we present a collection of five papers on visual and multimedia information management and retrieval topics, addressing some aspects of these challenges. These papers have been selected from the conference proceedings (Kluwer Academic Publishers, ISBN: 1-4020- 7060-8) of the Sixth IFIP 2.6 Working Conference on Visual Database Systems (VDB6), held in Brisbane, Australia, on 29–31 May 2002.

Crisis, change, and institutions in forest management: the Wet Tropics of northeastern Australia

This paper tests the four-phase heuristic model of change in resource management regimes developed by Gunderson et al. (1995. In: Barriers and Bridges to the Renewal of Ecosystems and Institutions. Columbia University Press, New York, pp. 489-533) by applying it to a case analysis of rainforest management in northeastern Australia. The model suggests that resource management regimes change in four phases: (i) crisis caused by external factors, (ii) a search for alternative management solutions, (iii) creation of a new management regime, and (iv) bureaucratic implementation of the new arrangements. The history of human use arid management of the tropical forests of this region is described and applied to this model. The ensuing analysis demonstrates that: (i) resource management tends to be characterized by a series of distinct eras; (ii) changes to management regimes are precipitated by crisis; and (iii) change is externally generated. The paper concludes by arguing that this theoretical perspective oil institutional change in resource management systems has wider utility. (C) 2002 Elsevier Science Ltd. All rights reserved.

Field hydraulic properties of an Alfisol under various fallow systems in southwestern Nigeria

The effects of various fallow management systems and cropping intensities on water infiltration were measured on an Alfisol at Ibadan in southwestern Nigeria. The objective was to determine the influence of the land use systems (a combination of crop-fallow sequences and intercropping types) on soil hydraulic properties obtained by disc permeameter and double-ring infiltration measurements. The experiment was established in 1989 as a split-plot design with four replications. The main plots were natural fallow, planted Pueraria phaseoloides and planted Leucaena leucocephala. The subplots were 1 year of maize/cassava intercrop followed by 3-year fallow (25% cropping intensity), or 2-year fallow (33% cropping intensity), or 1-year fallow (50% cropping intensity), or no fallow period (100% cropping intensity). Water infiltration rates and sorptivities were measured under saturated and unsaturated flow. Irrespective of land use, infiltration rates at the soil surface (121-324 cm h(-1)) were greater than those measured at 30 cm depth (55-144 cm h(-1)). This indicated that fewer large pores were present below 30 cm depth compared with 0-30 cm, depth. Despite some temporal variation, sorptivities with the highest mean value of 93.5 cm h(-1/2) increased as the cropping intensity decreased, suggesting a more continuous macropore system under less intensive land use systems. This was most likely due to continuous biopores created by perennial vegetation under long fallow systems. Intercropped maize and cassava yields also increased as cropping intensity decreased. The weak relationship between crop yields and hydraulic conductivity/infiltration rates suggests that the rates were not limiting.

Integrating science and management through collaborative learning and better information management

Achieving more sustainable land and water use depends on high-quality information and its improved use. In other words, better linkages are needed between science and management. Since many stakeholders with different relationships to the natural resources are inevitably involved, we suggest that collaborative learning environments and improved information management are prerequisites for integrating science and management. Case studies that deal with resource management issues are presented that illustrate the creation of collaborative learning environments through systems analyses with communities, and an integration of scientific and experiential knowledge of components of the system. This new knowledge needs to be captured and made accessible through innovative information management systems designed collaboratively with users, in forms which fit the users' 'mental models' of how their systems work. A model for linking science and resource management more effectively is suggested. This model entails systems thinking in a collaborative learning environment, and processes to help convergence of views and value systems, and make scientists and different kinds of managers aware of their interdependence. Adaptive management provides a mechanism for applying and refining scientists' and managers' knowledge. Copyright (C) 2003 John Wiley Sons, Ltd.

Ex ante evaluations of alternate data structures for end user queries: Theory and experimental test

The data structure of an information system can significantly impact the ability of end users to efficiently and effectively retrieve the information they need. This research develops a methodology for evaluating, ex ante, the relative desirability of alternative data structures for end user queries. This research theorizes that the data structure that yields the lowest weighted average complexity for a representative sample of information requests is the most desirable data structure for end user queries. The theory was tested in an experiment that compared queries from two different relational database schemas. As theorized, end users querying the data structure associated with the less complex queries performed better Complexity was measured using three different Halstead metrics. Each of the three metrics provided excellent predictions of end user performance. This research supplies strong evidence that organizations can use complexity metrics to evaluate, ex ante, the desirability of alternate data structures. Organizations can use these evaluations to enhance the efficient and effective retrieval of information by creating data structures that minimize end user query complexity.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Adaptive management and its role in managing Great Barrier Reef water quality

Adaptive management is the pathway to effective conservation, use and management of Australia’s coastal catchments and waterways. While the concepts of adaptive management are not new, applications involving both assessment and management responses are indeed limited at the national and regional scales. This paper outlines the components of a systematic framework for linking scientific knowledge, existing tools, planning approaches and participatory processes to achieve healthy regional partnerships between community, industry, government agencies and science providers to overcome institutional barriers and uncoordinated monitoring. The framework developed by the Coastal CRC (www.coastal.crc.org.au/amf/amf_index.htm) is hierarchical in the way it displays information to allow associated frameworks to be integrated, and represents a construct in which processes, information, decision tools and outcomes are brought together in a structured and transparent way for adaptive catchment and coastal management. This paper proposes how an adaptive management approach could be used to benefit the implementation of the Reef Water Quality Protection Plan (RWQPP).

SCORPION2: A database for structure-function analysis of scorpion toxins

Scorpion toxins are important experimental tools for characterization of vast array of ion channels and serve as scaffolds for drug design. General public database entries contain limited annotation whereby rich structure-function information from mutation studies is typically not available. SCORPION2 contains more than 800 records of native and mutant toxin sequences enriched with binding affinity and toxicity information, 624 three-dimensional structures and some 500 references. SCORPION2 has a set of search and prediction tools that allow users to extract and perform specific queries: text searches of scorpion toxin records, sequence similarity search, extraction of sequences, visualization of scorpion toxin structures, analysis of toxic activity, and functional annotation of previously uncharacterized scorpion toxins. The SCORPION2 database is available at http://sdmc.i2r.a-star.edu.sg/scorpion/. (c) 2006 Elsevier Ltd. All rights reserved.

Multiresolution query optimization in an online environment

Multiresolution (or multi-scale) techniques make it possible for Web-based GIS applications to access large dataset. The performance of such systems relies on data transmission over network and multiresolution query processing. In the literature the latter has received little research attention so far, and the existing methods are not capable of processing large dataset. In this paper, we aim to improve multiresolution query processing in an online environment. A cost model for such query is proposed first, followed by three strategies for its optimization. Significant theoretical improvement can be observed when comparing against available methods. Application of these strategies is also discussed, and similar performance enhancement can be expected if implemented in online GIS applications.

Towards effective indexing for very large video sequence database

With rapid advances in video processing technologies and ever fast increments in network bandwidth, the popularity of video content publishing and sharing has made similarity search an indispensable operation to retrieve videos of user interests. The video similarity is usually measured by the percentage of similar frames shared by two video sequences, and each frame is typically represented as a high-dimensional feature vector. Unfortunately, high complexity of video content has posed the following major challenges for fast retrieval: (a) effective and compact video representations, (b) efficient similarity measurements, and (c) efficient indexing on the compact representations. In this paper, we propose a number of methods to achieve fast similarity search for very large video database. First, each video sequence is summarized into a small number of clusters, each of which contains similar frames and is represented by a novel compact model called Video Triplet (ViTri). ViTri models a cluster as a tightly bounded hypersphere described by its position, radius, and density. The ViTri similarity is measured by the volume of intersection between two hyperspheres multiplying the minimal density, i.e., the estimated number of similar frames shared by two clusters. The total number of similar frames is then estimated to derive the overall similarity between two video sequences. Hence the time complexity of video similarity measure can be reduced greatly. To further reduce the number of similarity computations on ViTris, we introduce a new one dimensional transformation technique which rotates and shifts the original axis system using PCA in such a way that the original inter-distance between two high-dimensional vectors can be maximally retained after mapping. An efficient B+-tree is then built on the transformed one dimensional values of ViTris' positions. Such a transformation enables B+-tree to achieve its optimal performance by quickly filtering a large portion of non-similar ViTris. Our extensive experiments on real large video datasets prove the effectiveness of our proposals that outperform existing methods significantly.

Towards user profiling for web recommendation

Collaborative recommendation is one of widely used recommendation systems, which recommend items to visitor on a basis of referring other's preference that is similar to current user. User profiling technique upon Web transaction data is able to capture such informative knowledge of user task or interest. With the discovered usage pattern information, it is likely to recommend Web users more preferred content or customize the Web presentation to visitors via collaborative recommendation. In addition, it is helpful to identify the underlying relationships among Web users, items as well as latent tasks during Web mining period. In this paper, we propose a Web recommendation framework based on user profiling technique. In this approach, we employ Probabilistic Latent Semantic Analysis (PLSA) to model the co-occurrence activities and develop a modified k-means clustering algorithm to build user profiles as the representatives of usage patterns. Moreover, the hidden task model is derived by characterizing the meaningful latent factor space. With the discovered user profiles, we then choose the most matched profile, which possesses the closely similar preference to current user and make collaborative recommendation based on the corresponding page weights appeared in the selected user profile. The preliminary experimental results performed on real world data sets show that the proposed approach is capable of making recommendation accurately and efficiently.