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Many mobile devices today are equipped with diversified sensors that enable the acquisition of rich user context (e.g. GPS location, phone activity) for application utilization. With the growing usage of mobile devices in daily life, the problem of conveniently and promptly searching a piece of content that a user has viewed on his/her device before becomes more and more crucial. This paper aims to propose a context‐based query processing framework called UCQP that supports unstructured queries for content search in a user's access history.

Beyond the keywords related to the content properties, a context query in the framework is specified with freeform phrases that describe high‐level mobile contexts of the user at a previous time when the user viewed the searched content.

Experimental results on a prototype system of the framework illustrate its good accuracy and small response time.

To tolerate the incompleteness and inaccuracy in user query texts caused by fading human memory, the authors develop several semantic query parsers that are tailored for different types of contexts using natural language processing and information retrieval techniques. The authors further propose a similarity model to rank the multiple result contents of a query by comparing context entities specified in the query and historical context values associated with each result.

The purpose of this paper is to characterise a number of current and future computing environments and summarises their resource discovery requirements. It then seeks to analyse, with respect to the requirements of each environment, several established service discovery protocols and some newer protocols that are still in the research domain. In addition, the key features of a new resource discovery protocol that has been developed to operate with heterogeneous computing environments are described.

A comprehensive literature survey was undertaken, highlighting the shortcomings of existing resource discovery protocols with respect to large pervasive computing environments. Given the identified gaps in existing protocols, an alternative protocol is suggested.

The main findings of this paper relate to the identified shortcomings of existing resource discovery protocols. It was also found that a hybrid resource discovery protocol capable of spanning dynamic, mobile computing environments and more stable ones was able to overcome many of the challenges presented by large‐scale pervasive computing environments.

This paper presents comprehensive literature survey of the state‐of‐the‐art in resource discovery protocols, pointing out some of the problems that are not solved. The paper describes the design of an alternative protocol, and presents an evaluation of it. The pervasive computing research community can draw upon the survey and evaluation to guide the design of future resource discovery protocols for the increasingly dynamic world in which we live.

The purpose of this paper is to develop a new summarisation approach, namely structure‐preserving and query‐biased summarisation, to improve the effectiveness of web searching. During web searching, one aid for users is the document summaries provided in the search results. However, the summaries provided by current search engines have limitations in directing users to relevant documents.

The proposed system consists of two stages: document structure analysis and summarisation. In the first stage, a rule‐based approach is used to identify the sectional hierarchies of web documents. In the second stage, query‐biased summaries are created, making use of document structure both in the summarisation process and in the output summaries.

In structural processing, about 70 per cent accuracy in identifying document sectional hierarchies is obtained. The summarisation method is tested on a task‐based evaluation method using English and Turkish document collections. The results show that the proposed method is a significant improvement over both unstructured query‐biased summaries and Google snippets in terms of f‐measure.

The proposed summarisation system can be incorporated into search engines. The structural processing technique also has applications in other information systems, such as browsing, outlining and indexing documents.

In the literature on summarisation, the effects of query‐biased techniques and document structure are considered in only a few works and are researched separately. The research reported here differs from traditional approaches by combining these two aspects in a coherent framework. The work is also the first automatic summarisation study for Turkish targeting web search.

To provide mutual anonymity over traditionally un‐anonymous Distributed Hash Tables (DHT) based Peer‐to‐Peer overlay networks, while maintaining the desired scalability and guaranteed lookup properties of the DHTs.

Agyaat uses a novel hybrid‐overlay design, a fully decentralized topology without any trusted proxies. It anonymizes both the querying and responding peers through the use of unstructured topologies, called clouds, which are added onto the structured overlays. In addition, it regulates the cloud topologies to ensure the guaranteed location of data and scalability of routing. A unique characteristic of the design is the ability of users to tradeoff between desired anonymity and performance. The paper presents a thorough performance and anonymity analysis of the system, and also analyzes few anonymity compromising attacks and countermeasures.

The results indicate that Agyaat is able to provide mutual anonymity while maintaining the scalability of lookups, affecting the costs only by a constant factor.

While Agyaat is able to meet its mutual anonymity and performance goals, there exist other security vulnerabilities like possible Denial‐of‐Service (DoS) attacks, both due to its design and the underlying DHT overlay. This is fertile ground for future work.

Agyaat uses a novel topology architecture and associated protocols that are conducive to providing mutually anonymous services.

Resilient distributed processing technique (RDPT), in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.

The proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.

Query processing in Hadoop influences the distributed processing with the MapReduce model. MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers. Its results are valid for some extent size of the data.

Pig supports the required parallel processing framework with the following constructs during the processing of queries: FOREACH; FLATTEN; COGROUP.

Sizing functions are crucial inputs for unstructured mesh generation since they determine the element distributions of resulting meshes to a large extent. Meanwhile, automating the procedure of creating a sizing function is a prerequisite to set up a fully automatic mesh generation pipeline. In this paper, an automatic algorithm is proposed to create a high-quality sizing function for an unstructured surface and volume mesh generation by using a triangular mesh as the background mesh.

A practically efficient and effective solution is developed by using local operators carefully to re-mesh the tessellation of the input Computer Aided Design (CAD) models. A nonlinear programming (NLP) problem has been formulated to limit the gradient of the sizing function, while in this study, the object function of this NLP is replaced by an analytical equation that predicts the number of elements. For the query of the sizing value, an improved algorithm is developed by using the axis-aligned bounding box (AABB) tree structure.

The local operations of re-meshing could effectively and efficiently resolve the banding issue caused by using the default tessellation of the model to define a sizing function. Experiments show that the solution of the revised NLP, in most cases, could provide a better solution at the lower cost of computational time. With the help of the AABB tree, the sizing function defined at a surface background mesh can be also used as the input of volume mesh generation.

Theoretical analysis reveals that the construction of the initial sizing function could be reduced to the solution of an optimization problem. The definitions of the banding elements and surface proximity are also given. Under the guidance of this theoretical analysis, re-meshing and ray-casting technologies are well-designed to initial the sizing function. Smoothing with the revised NLP and querying by the AABB tree, the paper provides an automatic method to get a high-quality sizing function for both surface and volume mesh generation.

In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of EXtensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user‐defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi‐structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three‐fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a viewdriven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction.

The purpose of this paper is to investigate the effects of verbal-imagery cognitive styles of information searching behavior of users in using the Web.

In all, 44 participants were recruited for this study. The participants’ cognitive styles were measured by using Riding's Cognitive Style Analysis test. Three search tasks were designed based on Kim's search task definitions. Moreover, an individual lab session was arranged and then participants’ memos were analyzed using content analysis.

In all, 48 strategies in four categories of behaviors in searching the Web were identified. There were associations between users’ cognitive styles and their information searching behavior. The participants’ selection of the search initiation behaviors varied, so that imagers suffered from more varied initial behavior than verbalizers. The verbalizers tended to search in a narrow area, then broadening the area and following structured navigation and reading behavior to process information, while imagers tended to search in a general area, then narrowing down the search and adopting mixed navigational styles and mixed behaviors to process information. This study revealed that there was a difference in search performance of verbalizers and imagers descriptively, as verbalizers spent more time compared to imagers and imagers visited more nodes than verbalizers for the tasks completion. In addition, the task was an important variable influencing the search performance. Based on the key findings (search initiation behaviors, formulating search queries, navigational behaviors, information processing behaviors), a conceptual pattern of Web searching and cognitive styles is presented.

The study provides a new understanding of Web users’ information search behavior based on cognitive styles which contributes to the theoretical basis of Web search research. It also raises various questions within the context of user studies

The paper adopted a mixed approach in the area of information searching on the Web. A valuable contribution lies in the methods developed.

The purpose of the paper is to review the characteristics of systems that combine P2P technology with explicit ontologies and assess the benefits of these technologies for inter‐organizational knowledge management.

We characterize existing technologies with respect to a number of aspects that are relevant to knowledge management on a technical level. We further provide an example of an existing system and categorize it according to the aspects.

We conclude that ontology‐based P2P systems are in general beneficial for distributed knowledge management systems and that the design of such systems can be guided using the aspects we distinguish.

The paper presents the first attempt to rigorously identify and discuss the design space of ontology‐based P2P systems.