When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Edge Influence Computation in Dynamic Graphs

Reachability queries are of great importance in many research and application areas, including general graph mining, social network analysis and so on. Many approaches have been proposed to compute whether there exists one path from one node to another node in a graph. Most of these approaches focus on static graphs, however in practice dynamic graphs are more common. In this paper, we focus on handling graph reachability queries in dynamic graphs. Specifically we investigate the influence of a given edge in the graph, aiming to study the overall reachability changes in the graph brought by the possible failure/deletion of the edge. To this end, we firstly develop an efficient update algorithm for handling edge deletions. We then define the edge influence concept and put forward a novel computation algorithm to accelerate the computation of edge influence. We evaluate our approach using several real world datasets. The experimental results show that our approach outperforms traditional approaches significantly

Efficient Computation of Distance Labeling for Decremental Updates in Large Dynamic Graphs

Since today's real-world graphs, such as social network graphs, are evolving all the time, it is of great importance to perform graph computations and analysis in these dynamic graphs. Due to the fact that many applications such as social network link analysis with the existence of inactive users need to handle failed links or nodes, decremental computation and maintenance for graphs is considered a challenging problem. Shortest path computation is one of the most fundamental operations for managing and analyzing large graphs. A number of indexing methods have been proposed to answer distance queries in static graphs. Unfortunately, there is little work on answering such queries for dynamic graphs. In this paper, we focus on the problem of computing the shortest path distance in dynamic graphs, particularly on decremental updates (i.e., edge deletions). We propose maintenance algorithms based on distance labeling, which can handle decremental updates efficiently. By exploiting properties of distance labeling in original graphs, we are able to efficiently maintain distance labeling for new graphs. We experimentally evaluate our algorithms using eleven real-world large graphs and confirm the effectiveness and efficiency of our approach. More specifically, our method can speed up index re-computation by up to an order of magnitude compared with the state-of-the-art method, Pruned Landmark Labeling (PLL)

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

Supporting and structuring "contributing student pedagogy" in computer science curricula

Contributing student pedagogy (CSP) builds upon social constructivist and community-based learning principles to create engaging and productive learning experiences. What makes CSP different from other, related, learning approaches is that it involves students both learning from and also explicitly valuing the contributions of other students. The creation of such a learning community builds upon established educational psychology that encourages deep learning, reflection and engagement. Our school has recently completed a review and update of its curriculum, incorporating student content-creation and collaboration into the design of key courses across the curriculum. Our experiences, based on several years of experimentation and development, support CSP-based curriculum design to reinforce the value of the student perspective, the clear description of their own transformative pathway to knowledge and the importance of establishing student-to-student networks in which students are active and willing participants. In this paper, we discuss the tools and approaches that we have employed to guide, support and structure student collaboration across a range of courses and year levels. By providing an account of our intentions, our approaches and tools, we hope to provide useful and transferrable knowledge that can be readily used by other academics who are considering this approach.Katrina Falkner and Nickolas J.G. Falkne