Building a Web-Enabled Multimedia Data Warehouse

In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of computer users (Yang & Hurson, 2006). The integration of wireless communication, pervasive computing, and ubiquitous data processing with multimedia database systems has enabled the connection and fusion of distributed multimedia data sources. In addition, the emerging applications, such as smart classroom, digital library, habitat/environment surveillance, traffic monitoring, and battlefield sensing, have provided increasing motivation for conducting research on multimedia content representation, data delivery and dissemination, data fusion and analysis, and contentbased retrieval. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought—video, audio, and images (Karpouzis, Raouzaiou, Tzouveli, Iaonnou, & Kollias, 2003; Liu, Bao, Yu, & Xu, 2005; Yang & Hurson, 2005). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations such as imprecise query or content-based retrieval. For instance, the image database systems may accept an example picture and return the most similar images of the example (Cox, Miller, & Minka, 2000; Hsu, Chua, & Pung, 2000; Huang, Chang, & Huang, 2003). However, the conveniences of multimedia applications come with challenges to the existing data management schemes: • Efficiency: Multimedia applications generally require more resources; however, the storage space and processing power are limited in many practical systems, for example, mobile devices and wireless networks (Yang & Hurson, 2005). Due to the large data volume and complicated operations of multimedia applications, new methods are needed to facilitate efficient representation, retrieval, and processing of multimedia data while considering the technical constraints. • Semantic Gap: There is a gap between user perception of multimedia entities and physical representation/access mechanism of multimedia data. Users often browse and desire to access multimedia data at the object level (“entities” such as human beings, animals, or buildings). However, the existing multimedia retrieval systems tend to access multimedia data based on their lower-level features (“characteristics” such as color patterns and textures), with little regard to combining these features into data objects. This representation gap often leads to higher processing cost and unexpected retrieval results. The representation of multimedia data according to human’s perspective is one of the focuses in recent research activities; however, few existing systems provide automated identification or classification of objects from general multimedia collections. • Heterogeneity: The collections of multimedia data are often diverse and poorly indexed. In a distributed environment, because of the autonomy and heterogeneity of data sources, multimedia data objects are often represented in heterogeneous formats. The difference in data formats further leads to the difficulty of incorporating multimedia data objects under a unique indexing framework. • Semantic Unawareness: The present research on content-based multimedia retrieval is based on feature vectors—features are extracted from audio/video streams or image pixels, empirically or heuristically, and combined into vectors according to the application criteria. Because of the application-specific multimedia formats, the feature-based paradigm lacks scalability and accuracy.

In this paper, we present a novel approach for multimedia data indexing and retrieval that is machine independent and highly flexible for sharing multimedia data across applications. Traditional multimedia data indexing and retrieval problems have been attacked using the central data server as the main focus, and most of the indexing and query-processing for retrieval are highly application dependent. This precludes the use of created indices and query processing mechanisms for multimedia data which, in general, have a wide variety of uses across applications. The approach proposed in this paper addresses three issues: 1. multimedia data indexing; 2. inference or query processing; and 3. combining indices and inference or query mechanism with the data to facilitate machine independence in retrieval and query processing. We emphasize the third issue, as typically multimedia data are huge in size and requires intra-data indexing. We describe how the proposed approach addresses various problems faced by the application developers in indexing and retrieval of multimedia data. Finally, we present two applications developed based on the proposed approach: video indexing; and video content authorization for presentation.

Warehousing for a decision support is a technique for retrieval and integration of data from distributed, autonomous, possibly heterogeneous, information sources. A data warehouse collects integrated information, available for queries and analysis. A materialized view of the system is kept at a site called the data warehouse, and user queries are processed using this view. The view has to be maintained to reflect the updates done against the base relations stored at the various data sources. A more efficient solution to be reflected updates done at each source is to materialize the relevant subsets of the data at the base relations pertaining to the actual view and process user queries against this local data. The updates done against the base relations are propagated to the materialized view and the view is maintained incrementally. However, earlier algorithms for incremental view maintenance either required a quiescent state at the data warehouse or required exponential message amounts in terms of the data sources. Therefore, in this paper, we proposed an enhanced algorithm and the algorithm instead does not require that the data warehouse be in a quiescent state for incorporating the new views and also the message amount between warehouse and sources can be reduced.

Machine learning techniques have been used extensively to build models for the analysis and retrieval of multimedia data. The explosion of multimedia data on the Web poses a great challenge to such techniques not simply because of the sheer data volume, but also because of the heterogeneity of the data. With data from a wide variety of domains, models trained from one domain do not generalize well to other domains, while at the same time it is prohibitively expensive to build new models for each and every domain due to the high cost for labeling training examples. In this paper, we tackle the heterogeneity challenge in large-scale multimedia data using cross-domain model adaptation for better performance and reduced human cost. Specifically, we investigate the problem of adapting supervised classifiers trained from one or more source domains to a new classifier for a target domain that has only limited labeled examples. The foundation of our work is a general framework for function-level classifier adaptation based on the regularized loss minimization principle, which adapts a classifier by directly modifying its decision function. Under this framework, one can derive concrete adaptation algorithms by plugging in any loss and regularization functions, among which we elaborate on adaptive support vector machines (a-SVM). We further extend this framework for multiclassifier adaptation, namely adapting multiple existing classifiers into a classifier for the target domain, in a way that the contributions of these existing classifiers are automatically determined. We evaluate the proposed approaches in cross-domain semantic concept detection based on TRECVID corpora. The results show that our approaches outperform existing (adaptation and nonadaptation) methods in terms of accuracy and/or efficiency, and adaptation from multiple classifiers offers further benefits.

Distributed multimedia presentation systems require flexibility and good quality of service for multimedia data presentations. To ensure flexible and satisfiable presentations of multimedia data, collaborations between servers, network, and clients must be carefully designed to retrieve the data from the disk and transfer the data to the client. In this paper we investigate a multi-level buffering and feedback scheme to guarantee the on-time retrieval and transmission of multimedia data in the distributed multimedia presentation environments. In the multi-level buffering scheme, both client and server buffers are designed and collaborated to support the end-to-end multimedia data transmission. These buffers can absorb the rate variations of media streams in different locations. Local and remote feedback messages are used to dynamically prevent buffer overflow/underflow problems. Experiments demonstrate that highly smooth and synchronized presentations of multimedia streams can be supported.

As the proliferation of multimedia systems continues in diverse application areas, it is becoming increasingly apparent that the performance of the I/O subsystem is a critical limiting factor in the usefulness of such systems. This has spurred extensive research to discover and design efficient and robust I/O systems for the storage and retrieval of multimedia data. To mitigate the effects of the I/O bottleneck in multimedia application environments, we must employ novel technologies and efficient algorithms, and we must use available resources carefully. This paper identifies some significant issues involved and presents a survey of the techniques developed or proposed during recent years to make multimedia I/O more efficient.

Continuing strides in processing, storage, sensing, and networking technologies are enabling people to capture their activities and experiences as greater volumes of ever-richer media. A big emerging challenge today is the organization, retrieval, and exploitation of such multimedia data surrounding the activities of individuals or enterprises. The field of multimedia electronic chronicles deals with the unified contextual organization, presentation, and analysis of temporal streams of multimedia data captured by individuals, groups, or organizations. The value of electronic chronicles is in converting activity and experience from the past into actionable intelligence in the present. Such multimedia electronic chronicles, with their associated techniques for search and navigation, analysis and reasoning, and prediction and alerting, will have enormous impact on various spheres of life spanning enhancement of personal life, business productivity, entertainment, and government operations. This paper, which serves as a companion to a keynote talk by the first author at ICME 2005, explains the notion of electronic chronicles and outlines the research challenges in this area

Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories. Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a probabilistic framework for semantic video indexing, which call support filtering and retrieval and facilitate efficient content-based access. To map low-level features to high-level semantics we propose probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using probabilistic models for six site multijects, rocks, sky, snow, water-body forestry/greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning.

In this paper, we present the novel scheduling algorithm of the multimedia data retrieval for the mobile disk drive. Our algorithm is focused on minimizing the power consumption in multimedia data retrieval. While the disk based storage devices, e.g. hard disk and optical disk becomes small enough to be used in mobile devices, the practical usage of which leaves much to be desired due to the stringent power consumption restriction of the mobile device. The playback of multimedia data requires that data blocks are delivered to the destination in periodic fashion. The major issue here has been how to guarantee the continuous flow of data. Most of preceding works assume that the disk drive always operates in the steady state. However, this does not hold in modern disk drive for the mobile device. Modern low power disk drive for mobile device goes into standby state when it is not in use. While this feature can significantly extend the battery life, it adds another dimension of complexity in scheduling of the multimedia data retrieval. We elaborately model the power consumption behavior of the low power mobile drive and develop an Adaptive Round Merge(ARM) scheduling algorithm which guarantees a certain disk bandwidth for the multimedia playback while minimizing the power consumption of the storage device. According to our simulation based experiment, the ARM algorithm reduces the power consumption by as much as 23%. It manifests itself when the video clip is relatively short, typically less than 30 sec.

Data Warehouses (DWs) are currently considered to be the cornerstone of Business Intelligence (BI) systems. Security is a key issue in DWs since the business information that they manage is crucial and highly sensitive, and should be carefully protected. However, the increasing amount of data available on the Web signifies that more and more DW systems are considering the Web as the primary data source through which to populate their DWs. XML is therefore widely accepted as being the principal means through which to provide easier data and metadata interchange among heterogeneous data sources from the Web and the DW systems.Although security issues have been considered during the whole development process of traditional DWs, current research lacks approaches with which to consider security when the target platform is based on the Web and XML technologies. The idiosyncrasy of the unstructured and semi-structured data available on the Web definitely requires particular security rules that are specifically tailored to these systems in order to permit their particularities to be captured correctly.In order to tackle this situation, in this paper, we propose a methodological approach based on the Model Driven Architecture (MDA) for the development of Secure XML DWs. We therefore specify a set of transformation rules that are able to automatically generate not only the corresponding XML structure of the DW from secure conceptual DW models, but also the security rules specified within the DW XML structure, thus allowing us to implement both aspects simultaneously. A case study is provided at the end of the paper to show the benefits of our approach.

Data warehousing is a fundamental component of modern business intelligence (BI) systems, enabling organizations to store, manage, and analyze vast amounts of data for strategic decision-making. It involves the integration of data from disparate sources into a centralized repository, optimized for efficient querying and reporting. The process of Extract, Transform, and Load (ETL) ensures that data is cleaned, transformed, and loaded into the warehouse for consistent and high-quality analysis. By leveraging data warehouses, businesses gain valuable insights into historical trends, optimize operational performance, and make data-driven decisions. However, data warehousing also presents challenges, including the complexity of data integration, high implementation costs, and data security concerns. Looking ahead, advancements in cloud computing, real-time data processing, and artificial intelligence are expected to revolutionize data warehousing, enabling more scalable, efficient, and insightful data management solutions. This paper explores the key components, benefits, challenges, and future trends of data warehousing, emphasizing its crucial role in enhancing organizational intelligence and competitiveness.

Recently, research has been active in various fields in relation to multimedia data retrieval to reduce the accuracy and calculation costs of multimedia retrieval. In particular, various search methods using multimedia has been developed. However, in many applications currently operating in the system, large multimedia data is distributed across servers or sites at different locations, so assembling multimedia data only as a hash presents huge computational, communication, and storage costs. In this paper, when searching for multimedia information stored in distributed environments using IoT devices, multiple hash chain-based multimedia retrieval techniques are proposed to minimize accuracy and calculation costs. The proposed technique is aimed at improving the accuracy of multimedia by using IoT devices to divide-and-conquer multimedia information that is collected and stored distributed. The proposed technique provides probability values for each segmented multimedia information by disaggregating the various attribute information that is composed of multimedia as much as possible, thereby increasing the accuracy of the multimedia that users want. Suggestion techniques determine the group size of multimedia data according to the priority of layered multimedia data. Multimedia group data include multimedia data contained in higher layers and subdivides the various attribute information constituting multimedia data. In addition, the proposed techniques improve the accessibility of multimedia data over existing ones by linking priority information between multimedia data.

In data warehouse (DW) development, a series of mappings must be specified between user concepts and data source elements, in order to identify which sources must undergo an integration process. Until now, these mappings are either assumed to be implied by name matching or identified according to the designer's experience. Then, the result is implemented as Extraction/Transformation/Loading (ETL) processes. Since ETL processes relate elements at the logical level, designers cannot adequately analyze how a change in requirements or in the data sources affects the analysis capabilities. Furthermore, this approach makes it difficult to perform incremental changes in DW design, requiring in some cases to perform the whole analysis again. In this paper we present a set of semantic mappings that relate user concepts specified by requirements to those obtained from data sources. In turn, this allows us to accurately identify how any potential change affects the different structures and ETL processes. As a DW evolves over time, our approach easily allows us to incorporate new concepts, as well as any change introduced at requirements or data sources into the DW repository with no need to redesign the whole DW. In order to show the application of our proposal, we show a real case study focusing on the Digital library of the University of Alicante.

A Data warehouse (DW) is characterized by a complex architecture, designed in order to integrate data derived from operational data sources (DS), hence providing advanced analytical tools of these data. The DW is highly dependent on its DS. Hence, evolutions of the DS schema need to be propagated to the DW schema and content. This paper presents a model-driven approach for the evolution of a multidimensional DW. It is based on two evolution models: a first evolution model for the DS and another for the DW. These two models concern the data structure aspects as well as the evolution operations. The transition between these two models is performed through specific transformation rules defined in QVT (Query\View\Transformation).

In the past few years, several conceptual approaches have been proposed for the specification of the main multidimensional (MD) properties of the data warehouse (DW) repository. However, most of them deal with isolated aspects of the DW and do not provide designers with an integrated and standard method for designing the whole DW life cycle (ETL processes, data sources, DW repository and so on).Some approaches are depending on specific platform or neglecting important issues in DW design life cycle. Extraction-transformation-loading (ETL) processes play an important role in data warehouse architecture because they are responsible of integrating data from heterogeneous data sources into the DW repository. This paper proposes a conceptual model to refresh data warehouse by (insert, update, delete) data using ETL processes and considering DW security requirements. Firstly, the proposed ETL model is based on Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of ETL processes .secondly; this part focuses on how to integrate the proposed ETL model with the DW model. The proposed DW model depends on the Model Driven Architecture (MDA). MDA is a standard framework for software development that addresses the complete life cycle of designing, deploying, integrating, and managing applications by using models in software development. This paper proposes an integrated conceptual model for addressing temporal data warehouse security requirements (CMTDWS).