Effective scheduling algorithm for on-demand XML data broadcasts in wireless environments

The organization of data on wireless channels, which aims to reduce the access time of mobile clients, is a key problem in data broadcasts. Many scheduling algorithms have been designed to organize flat data on air. However, how to effectively schedule semi-structured information such as XML data on wireless channels is still a challenge. In this paper, we firstly propose a novel method to greatly reduce the tuning time by splitting query results into XML snippets and to achieve better access efficiency by combining similar ones. Then we analyze the data broadcast scheduling problem of on-demand XML data broadcasts and define the efficiency of a data item. Based on the definition, a Least Efficient Last (LEL) scheduling algorithm is also devised to effectively organize XML data on wireless channels. Finally, we study the performance of our algorithms through extensive experiments. The results show that our scheduling algorithms can reduce both access time and tuning time signifcantly when compared with existing work

Organizing XML data in a wireless broadcast system by exploiting structural similarities

Wireless data broadcast is an efficient way of delivering data of common interest to a large population of mobile devices within a proximate area, such as smart cities, battle fields, etc. In this work, we focus ourselves on studying the data placement problem of periodic XML data broadcast in mobile and wireless environments. This is an important issue, particularly when XML becomes prevalent in today’s ubiquitous and mobile computing devices and applications. Taking advantage of the structured characteristics of XML data, effective broadcast programs can be generated based on the XML data on the server only. An XML data broadcast system is developed and a theoretical analysis on the XML data placement on a wireless channel is also presented, which forms the basis of the novel data placement algorithm in this work. The proposed algorithm is validated through a set of experiments. The results show that the proposed algorithm can effectively place XML data on air and significantly improve the overall access efficiency

Air Indexing for On-Demand XML Data Broadcast

XML data broadcast is an efficient way to disseminate semistructured information in wireless mobile environments. In this paper, we propose a novel two-tier index structure to facilitate the access of XML document in an on-demand broadcast system. It provides the clients with an overall image of all the XML documents available at the server side and hence enables the clients to locate complete result sets accordingly. A pruning strategy is developed to cut down the index size and a two-tier structure is proposed to further remove any redundant information. In addition, two index distribution strategies, namely naive distribution and partial distribution, have been designed to interleave the index information with the XML documents in the wireless channels. Theoretical analysis and simulation experiments are also put forward to show the benefits of our indexing methods

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Leveraging Resources on Anonymous Mobile Edge Nodes

Smart devices have become an essential component in the life of mankind. The quick rise of smartphones, IoTs, and wearable devices enabled applications that were not possible few years ago, e.g., health monitoring and online banking. Meanwhile, smart sensing laid the infrastructure for smart homes and smart cities. The intrusive nature of smart devices granted access to huge amounts of raw data. Researchers seized the moment with complex algorithms and data models to process the data over the cloud and extract as much information as possible. However, the pace and amount of data generation, in addition to, networking protocols transmitting data to cloud servers failed short in touching more than 20% of what was generated on the edge of the network. On the other hand, smart devices carry a large set of resources, e.g., CPU, memory, and camera, that sit idle most of the time. Studies showed that for plenty of the time resources are either idle, e.g., sleeping and eating, or underutilized, e.g. inertial sensors during phone calls. These findings articulate a problem in processing large data sets, while having idle resources in the close proximity. In this dissertation, we propose harvesting underutilized edge resources then use them in processing the huge data generated, and currently wasted, through applications running at the edge of the network. We propose flipping the concept of cloud computing, instead of sending massive amounts of data for processing over the cloud, we distribute lightweight applications to process data on users\u27 smart devices. We envision this approach to enhance the network\u27s bandwidth, grant access to larger datasets, provide low latency responses, and more importantly involve up-to-date user\u27s contextual information in processing. However, such benefits come with a set of challenges: How to locate suitable resources? How to match resources with data providers? How to inform resources what to do? and When? How to orchestrate applications\u27 execution on multiple devices? and How to communicate between devices on the edge? Communication between devices at the edge has different parameters in terms of device mobility, topology, and data rate. Standard protocols, e.g., Wi-Fi or Bluetooth, were not designed for edge computing, hence, does not offer a perfect match. Edge computing requires a lightweight protocol that provides quick device discovery, decent data rate, and multicasting to devices in the proximity. Bluetooth features wide acceptance within the IoT community, however, the low data rate and unicast communication limits its use on the edge. Despite being the most suitable communication protocol for edge computing and unlike other protocols, Bluetooth has a closed source code that blocks lower layer in front of all forms of research study, enhancement, and customization. Hence, we offer an open source version of Bluetooth and then customize it for edge computing applications. In this dissertation, we propose Leveraging Resources on Anonymous Mobile Edge Nodes (LAMEN), a three-tier framework where edge devices are clustered by proximities. On having an application to execute, LAMEN clusters discover and allocate resources, share application\u27s executable with resources, and estimate incentives for each participating resource. In a cluster, a single head node, i.e., mediator, is responsible for resource discovery and allocation. Mediators orchestrate cluster resources and present them as a virtually large homogeneous resource. For example, two devices each offering either a camera or a speaker are presented outside the cluster as a single device with both camera and speaker, this can be extended to any combination of resources. Then, mediator handles applications\u27 distribution within a cluster as needed. Also, we provide a communication protocol that is customizable to the edge environment and application\u27s need. Pushing lightweight applications that end devices can execute over their locally generated data have the following benefits: First, avoid sharing user data with cloud server, which is a privacy concern for many of them; Second, introduce mediators as a local cloud controller closer to the edge; Third, hide the user\u27s identity behind mediators; and Finally, enhance bandwidth utilization by keeping raw data at the edge and transmitting processed information. Our evaluation shows an optimized resource lookup and application assignment schemes. In addition to, scalability in handling networks with large number of devices. In order to overcome the communication challenges, we provide an open source communication protocol that we customize for edge computing applications, however, it can be used beyond the scope of LAMEN. Finally, we present three applications to show how LAMEN enables various application domains on the edge of the network. In summary, we propose a framework to orchestrate underutilized resources at the edge of the network towards processing data that are generated in their proximity. Using the approaches explained later in the dissertation, we show how LAMEN enhances the performance of applications and enables a new set of applications that were not feasible

Efficient service discovery in wide area networks

Living in an increasingly networked world, with an abundant number of services available to consumers, the consumer electronics market is enjoying a boom. The average consumer in the developed world may own several networked devices such as games consoles, mobile phones, PDAs, laptops and desktops, wireless picture frames and printers to name but a few. With this growing number of networked devices comes a growing demand for services, defined here as functions requested by a client and provided by a networked node. For example, a client may wish to download and share music or pictures, find and use printer services, or lookup information (e.g. train times, cinema bookings). It is notable that a significant proportion of networked devices are now mobile. Mobile devices introduce a new dynamic to the service discovery problem, such as lower battery and processing power and more expensive bandwidth. Device owners expect to access services not only in their immediate proximity, but further afield (e.g. in their homes and offices). Solving these problems is the focus of this research. This Thesis offers two alternative approaches to service discovery in Wide Area Networks (WANs). Firstly, a unique combination of the Session Initiation Protocol (SIP) and the OSGi middleware technology is presented to provide both mobility and service discovery capability in WANs. Through experimentation, this technique is shown to be successful where the number of operating domains is small, but it does not scale well. To address the issue of scalability, this Thesis proposes the use of Peer-to-Peer (P2P) service overlays as a medium for service discovery in WANs. To confirm that P2P overlays can in fact support service discovery, a technique to utilise the Distributed Hash Table (DHT) functionality of distributed systems is used to store and retrieve service advertisements. Through simulation, this is shown to be both a scalable and a flexible service discovery technique. However, the problems associated with P2P networks with respect to efficiency are well documented. In a novel approach to reduce messaging costs in P2P networks, multi-destination multicast is used. Two well known P2P overlays are extended using the Explicit Multi-Unicast (XCAST) protocol. The resulting analysis of this extension provides a strong argument for multiple P2P maintenance algorithms co-existing in a single P2P overlay to provide adaptable performance. A novel multi-tier P2P overlay system is presented, which is tailored for service rich mobile devices and which provides an efficient platform for service discovery

Sustainable Paths for Data-Intensive Research Communities at the University of Melbourne: A Report for the Australian Partnership for Sustainable Repositories

This report presents the local project findings with a view to identifying how these findings may add to the knowledge base for informing an e-research strategy for the University of Melbourne. It also provides important considerations for how major Government initiatives in research policy and funding might impact on research data and records management requirements. Eleven research communities from diverse disciplines were consulted for an audit of their data management practices. Researchers from these communities represent a number of diverse disciplines: Applied Economics; Astrophysics; Computer Science and Software Engineering; Education; Ethnography; Experimental Particle Physics; Humanities informatics; Hydrology and Environmental Engineering; Linguistics; Medical informatics; Neuroscience and the Performing Arts. In addition to the specific findings for each group audited, the project findings also provide information about sustainability issues around research data management practices at the university

Modern Information Systems

The development of modern information systems is a demanding task. New technologies and tools are designed, implemented and presented in the market on a daily bases. User needs change dramatically fast and the IT industry copes to reach the level of efficiency and adaptability for its systems in order to be competitive and up-to-date. Thus, the realization of modern information systems with great characteristics and functionalities implemented for specific areas of interest is a fact of our modern and demanding digital society and this is the main scope of this book. Therefore, this book aims to present a number of innovative and recently developed information systems. It is titled "Modern Information Systems" and includes 8 chapters. This book may assist researchers on studying the innovative functions of modern systems in various areas like health, telematics, knowledge management, etc. It can also assist young students in capturing the new research tendencies of the information systems' development