Designing XML documents from conceptual schemas and workload information

Due to the increase of XML-based applications, XML schema design has become an important task. One approach is to consider conceptual schemas as a basis for generating XML documents compliant to consensual information of specific domains. However, the conversion of conceptual schemas to XML schemas is not a straightforward process and inconvenient design decisions can lead to a poor query processing on XML documents generated. This paper presents a conversion approach which considers data and query workload estimated for XML applications, in order to generate an XML schema from a conceptual schema. Load information is used to produce XML schemas which can respond well to the main queries of an XML application. We evaluate our approach through a case study carried out on a native XML database. The experimental results demonstrate that the XML schemas generated by our methodology contribute to a better query performance than related approaches.

RRSi: indexing XML data for proximity twig queries

Twig query pattern matching is a core operation in XML query processing. Indexing XML documents for twig query processing is of fundamental importance to supporting effective information retrieval. In practice, many XML documents on the web are heterogeneous and have their own formats; documents describing relevant information can possess different structures. Therefore some “user-interesting” documents having similar but non-exact structures against a user query are often missed out. In this paper, we propose the RRSi, a novel structural index designed for structure-based query lookup on heterogeneous sources of XML documents supporting proximate query answers. The index avoids the unnecessary processing of structurally irrelevant candidates that might show good content relevance. An optimized version of the index, oRRSi, is also developed to further reduce both space requirements and computational complexity. To our knowledge, these structural indexes are the first to support proximity twig queries on XML documents. The results of our preliminary experiments show that RRSi and oRRSi based query processing significantly outperform previously proposed techniques in XML repositories with structural heterogeneity.

A coherent query language for XML

Text search engines are inadequate for indexing and searching XML documents because they ignore metadata and aggregation structure implicit in the XML documents. On the other hand, the query languages supported by specialized XML search engines are very complex. In this paper, we present a simple yet flexible query language, and develop its semantics to enable intuitively appealing extraction of relevant fragments of information while simultaneously falling back on retrieval through plain text search if necessary. Our approach combines and generalizes several available techniques to obtain precise and coherent results.

Extracting knowledge from XML document repository: a semantic Web-based approach

XML plays an important role as the standard language for representing structured data for the traditional Web, and hence many Web-based knowledge management repositories store data and documents in XML. If semantics about the data are formally represented in an ontology, then it is possible to extract knowledge: This is done as ontology definitions and axioms are applied to XML data to automatically infer knowledge that is not explicitly represented in the repository. Ontologies also play a central role in realizing the burgeoning vision of the semantic Web, wherein data will be more sharable because their semantics will be represented in Web-accessible ontologies. In this paper, we demonstrate how an ontology can be used to extract knowledge from an exemplar XML repository of Shakespeare’s plays. We then implement an architecture for this ontology using de facto languages of the semantic Web including OWL and RuleML, thus preparing the ontology for use in data sharing. It has been predicted that the early adopters of the semantic Web will develop ontologies that leverage XML, provide intra-organizational value such as knowledge extraction capabilities that are irrespective of the semantic Web, and have the potential for inter-organizational data sharing over the semantic Web. The contribution of our proof-of-concept application, KROX, is that it serves as a blueprint for other ontology developers who believe that the growth of the semantic Web will unfold in this manner.

Intelligent Network Provisioning for Dynamically Downloadable Applications in Beyond 3G Mobile Networks

Mobile communications beyond 3G will integrate different (but complementary) access technologies into a common platform to deliver value-added services and multimedia content in an optimum way. However, the numerous possible configurations of mobile networks complicated the dynamic deployment of mobile applications. Therefore, research is intensely seeking a service provisioning framework that is technology-independent, supports multiple wireless network technologies, and can interwork high-level service management tasks to network management operations. This paper presents an open value chain paradigm, a model for downloadable applications and a mediating platform for service provisioning in beyond 3G mobile settings. Furthermore, we introduce mechanisms that support a coupled interaction between service deployment and network configuration operations, focusing on the dynamic provisioning of QoS state to data path devices according to the requirements of dynamically downloadable mobile value-added services (VAS).

XFlavor: providing XML features in media representation

We present XFlavor, a framework for providing XML representation of multimedia data. XFlavor can be used to convert multimedia data back and forth between binary and XML representations. Compared to bitstreams, XML documents are easier to access and manipulate, and consequently, the development of multimedia processing software is greatly facilitated, as one generic XML parser can be used to read and write different types of data in XML form.

Using a relational database for scalable XML search

XML is a flexible and powerful tool that enables information and security sharing in heterogeneous environments. Scalable technologies are needed to effectively manage the growing volumes of XML data. A wide variety of methods exist for storing and searching XML data; the two most common techniques are conventional tree-based and relational approaches. Tree-based approaches represent XML as a tree and use indexes and path join algorithms to process queries. In contrast, the relational approach utilizes the power of a mature relational database to store and search XML. This method relationally maps XML queries to SQL and reconstructs the XML from the database results. To date, the limited acceptance of the relational approach to XML processing is due to the need to redesign the relational schema each time a new XML hierarchy is defined. We, in contrast, describe a relational approach that is fixed schema eliminating the need for schema redesign at the expense of potentially longer runtimes. We show, however, that these potentially longer runtimes are still significantly shorter than those of the tree approach. We use a popular XML benchmark to compare the scalability of both approaches. We generated large collections of heterogeneous XML documents ranging in size from 500 MB to 8 GB using the XBench benchmark. The scalability of each method was measured by running XML queries that cover a wide range of XML search features on each collection. We measure the scalability of each method over different query features as the collection size increases. In addition, we examine the performance of each method as the result size and the number of predicates increase. Our results show that our relational approach provides a scalable approach to XML retrieval by leveraging existing relational database optimizations. Furthermore, we show that the relational approach typically outperforms the tree-based approach while scaling consistently over all collections studied.

Understanding and capturing people’s privacy policies in a mobile social networking application

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

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Agent-based multimedia presentation and adaptation service

In this paper, we aim to provide adaptive multimedia services especially video ones to end-users in an efficient and secure manner. Users moving outside the office should be able to maintain an office-like environment at their current locations. First, the agents within our proposed architecture negotiate the different communication and interaction factors autonomously and dynamically. Moreover, we needed to develop a user agent in addition to service and system agents that could negotiate the requirements and capabilities at run time to furnish best possible service results. Thus we designed and integrated a video indexing and key framing service within our overall agent-based architecture. We integrated this video indexing and content-based analysis service to adapt the video content according to run time conditions. We designed a video XML schema to validate the media content out of this multimedia service according to specific requirements and features, as we will describe later.

ARIS Method Extension for Business-Driven SOA

Service-oriented architecture (SOA) promises making companies more flexible enabling them to react quickly to changing market conditions. To leverage those advantages, SOA must be integrated with other existing approaches like business process management and enterprise architecture management. The authors describe how they extended the enterprise architecture management and business process management modelling method ARIS. They first created a SOA meta model by abstracting from existing modelling methods as well as extracting expert knowledge through interviews. In a second step, they mapped the SOA meta model to ARIS to provide a concrete modelling language for service-oriented enterprises. The ARIS extension allows describing and governing service architectures, describing services, linking service development initiatives to projects and company strategy, and discovering services for business process automation.

EXEM: Efficient XML data exchange management for mobile applications

In the past decade, the number of mobile devices has increased significantly. These devices are in turn showing more computational capabilities. It is therefore possible to envision a near future where client applications may be deployed on these devices. There are, however, constraints that hinder this deployment, especially the limited communication bandwidth and storage space available. This paper describes the Efficient XML Data Exchange Manager (EXEM) that combines context-dependent lossy and lossless compression mechanisms used to support lightweight exchange of objects in XML format between server and client applications. The lossy compression mechanism reduces the size of XML messages by using known information about the application. The lossless compression mechanism decouples data and metadata (compression dictionary) content. We illustrate the use of EXEM with a prototype implementation of the lossless compression mechanism that shows the optimization of the available resources on the server and the mobile client. These experimental results demonstrate the efficiency of the EXEM approach for XML data exchange in the context of mobile application development.

Design issues and performance comparisons in supporting the sockets interface over user-level communication architecture

Since user-level communication (ULC) architecture provides only primitive operations for application programmers, there have been several researches to build a portable and standard communication interface, such as sockets, on top of ULC architecture. Basically there are three different approaches to supporting the sockets interface over ULC architecture: LAN emulation, a user-level sockets, and a kernel-level sockets. The primary objective of this paper is to compare these approaches in terms of their design, implementation, and performance. We have developed and implemented a kernel-level sockets layer over ULC architecture, since there is currently no available implementation. We also present different design and implementation decisions on data receiving, data sending, connection management, etc. in the three approaches. Through the performance comparison, we show that LAN emulation approach exhibits the worst performance both in latency and bandwidth. Our experiments also show that a user-level sockets is useful for latency-sensitive applications and a kernel-level sockets is effective for applications which require high bandwidth and full compatibility with the legacy sockets interface.

Learning element similarity matrix for semi-structured document analysis

Capturing latent structural and semantic properties in semi-structured documents (e.g., XML documents) is crucial for improving the performance of related document analysis tasks. Structured Link Vector Mode (SLVM) is a representation recently proposed for modeling semi-structured documents. It uses an element similarity matrix to capture the latent relationships between XML elements—the constructing components of an XML document. In this paper, instead of applying heuristics to define the element similarity matrix, we propose to compute the matrix using the machine learning approach. In addition, we incorporate term semantics into SLVM using latent semantic indexing to enhance the model accuracy, with the element similarity learnability property preserved. For performance evaluation, we applied the similarity learning to k-nearest neighbors search and similarity-based clustering, and tested the performance using two different XML document collections. The SLVM obtained via learning was found to outperform significantly the conventional Vector Space Model and the edit-distance-based methods. Also, the similarity matrix, obtained as a by-product, can provide higher-level knowledge on the semantic relationships between the XML elements.

Behavioural adaptations to driver support systems: a modelling and road safety perspective

The design and implementation of new technologies dedicated to driver support and information management systems is the crucial transformation that has taken place in the field of automotive environment. While the development of these new systems has found a variety of sound and effective solutions, a number of fundamental questions and issues remain to be tackled and solved with respect to the consequences that such new technologies have on driver activity. In particular, a major concern is on “behavioural adaptation” and on the possible impact that this may play on the driving performance and ultimately on overall road safety. Within the European Project Adaptive Integrated Driver-vehicle InterfacE (AIDE), a specific research activity is dedicated (1) to the identification of crucial behavioural adaptation aspects associated with the use of new driver support systems and (2) of the specification of most relevant parameters that can be implemented in models for supporting design and safety assessment processes. This paper describes the results of a set of experiments carried out on a number of specific driving support systems and their results in terms of observed behavioural adaptation. These results will be imbedded in modelling architecture that enables to predict driver-vehicle-environment interactions in different dynamic conditions. Such model is also described in some details for what concerns the model characteristics and main parameters.

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Managing cooperation in e-business systems

E-Business management involves monitoring and controlling all forms of business transactions conducted over the Internet, extranets, and related communications technologies and services. As such, it includes the management of networks, systems, and software applications. E-Business management is beginning to depend increasingly on knowledge of the cooperative aspects of partners in a business chain and the cooperative aspects of stake-holders in e-business operations. The study of cooperation offers many opportunities to apply social network analyses to derive software solutions for the management of e-business. This article provides a methodology and case study that recognises the dimension of cooperation among a multiplicity of organisations, coined “Awareness-based Cooperative Management.” It is aimed towards the design or re-engineering of effective e-business management systems, where the key concept involves the degree of awareness held by e-business stake-holders. Awareness is defined in terms of stake-holder interactions and artefacts used in those interactions such as buyer specifications, requests for proposals, offers, service level agreements, and the like. Since awareness is an indispensable, albeit an inherently vague concept, we cast it and reason about it with the apparatus of fuzzy logic.

Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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Double Layer ACO Algorithm for the Multi-Objective FJSSP

Scheduling for the flexible job shop is very important in both fields of production management and combinatorial optimization. In this work, a double layer Ant Colony Optimization (ACO) algorithm is proposed for the Flexible Job Shop Scheduling Problem (FJSSP). In the proposed algorithm, two different ACO algorithms are applied to solve the FJSSP with a hierarchical way. The primary mission of upper layer ACO algorithm is achieving an excellent assignment of operations to machines. The leading task of lower layer ACO algorithm is obtaining the optimal sequencing of operations on each machine. Experimental results suggest that the proposed algorithm is a feasible and effective approach for the multi-objective FJSSP.

Scientific models and ethical issues in hybrid bionic systems research

Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories. Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and freedom. The analysis of these concerns requires reflecting on the availability of scientific models accounting for key aspects of sensorimotor coordination and plastic adaptation mechanisms in the brain.

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Adaptive training of video sets for image recognition on mobile phones

We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera-equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client–server-system for improving data acquisition and for supporting scale-invariant object recognition. A static as well as a dynamic training technique are presented that preprocess the collected object data differently and apply two types of neural networks (NN) for classification. Furthermore, the system enables a temporal adaptation for ensuring a continuous data acquisition to improve the recognition rate over time. A formal field experiment reveals current recognition rates and indicates the practicability of both methods under realistic conditions in a museum.

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Facial movement analysis in ASL

In the age of speech and voice recognition technologies, sign language recognition is an essential part of ensuring equal access for deaf people. To date, sign language recognition research has mostly ignored facial expressions that arise as part of a natural sign language discourse, even though they carry important grammatical and prosodic information. One reason is that tracking the motion and dynamics of expressions in human faces from video is a hard task, especially with the high number of occlusions from the signers’ hands. This paper presents a 3D deformable model tracking system to address this problem, and applies it to sequences of native signers, taken from the National Center of Sign Language and Gesture Resources (NCSLGR), with a special emphasis on outlier rejection methods to handle occlusions. The experiments conducted in this paper validate the output of the face tracker against expert human annotations of the NCSLGR corpus, demonstrate the promise of the proposed face tracking framework for sign language data, and reveal that the tracking framework picks up properties that ideally complement human annotations for linguistic research.

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