Bounding completion times of jobs with arbitrary release times and variable execution times
In many real-time systems, the workload can be characterized as a set of jobs with linear precedence constraints among them. Jobs often have variable execution times and arbitrary release times. We describe three algorithms that can be used to compute upper bounds on completion times of such jobs scheduled on a priority-driven basis. The algorithms have different performance and complexity. Simulation was performed to compare their performance.

title: Probabilistic performance guarantee for real-time tasks with varying computation times
abstract: Describes how the scheduling algorithms and schedulability analysis methods developed for periodic tasks can be extended to provide performance guarantees to semi-periodic tasks. Like periodic tasks, the requests in a semi-periodic task are released regularly. However, their computation times vary widely. We focus on systems where the total maximum utilization of the tasks on each processor is larger than one. Hence, according to the existing schedulability conditions for periodic tasks, we cannot guarantee that the semi-periodic tasks are schedulable, even though their total average utilization is very small. We describe two methods of providing probabilistic schedulability guarantees to the semi-periodic tasks. The first method, called probabilistic time-demand analysis, is a modification of the exact schedulability test for periodic tasks. The second method, called the transform-task method, transforms each task into a periodic task followed by a sporadic task. The transform-task method can provide an absolute guarantee to requests with shorter computation times and a probabilistic guarantee to the longer requests.

title: Synchronization protocols in distributed real-time systems
abstract: In many distributed real-time systems, the workload can be modeled as a set of periodic tasks, each of which consists of a chain of subtasks executing on different processors. Synchronization protocols are used to govern the release of subtasks so that the precedence constraints among subtasks are satisfied and the schedulability of the resultant system is analyzable. Tasks have different worst-case and average end-to-end response times when different protocols are used. In this paper, we consider distributed real-time systems with independent, periodic tasks and fixed-priority scheduling algorithms. We propose three synchronization protocols and conduct simulation to compare their performance with respect to the two timing aspects.

title: Analysis of large-scale topological properties for peer-to-peer networks
abstract: In This work we present a thorough study of the topological properties of the Gnutella network, which provides insight into the nature of the underlying system, helps us design high performance algorithms and generates more realistic topologies for simulation experiments. We compare two theoretical models of growing networks with the present real data of the Gnutella network.

title: Neighborhood formation and anomaly detection in bipartite graphs
abstract: Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (Neighborhood formation), and 2) finding abnormal nodes (Anomaly detection). And we propose algorithms to compute the neighborhood for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify abnormal nodes, using neighborhood information. We evaluate the quality of neighborhoods based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.

title: Object-Z web environment and projections to UML
abstract: This paper presents the XML/XSL approach to the development of a web environment for the formal speci cation language Object-Z. The projection techniques and tools from Object-Z (in XML) to UML (in XMI) are developed using XSL Transformations (XSLT). Furthermore, Object-Z (itself) is used to specify and design the essential functionalities of the web environment and the projection tools to UML. In a sense, the paper also demonstrates a formal approach to modeling web applications.

title: Selectivity estimation for predictive spatio-temporal queries
abstract: We propose a cost model for selectivity estimation of predictive spatio-temporal window queries. Initially, we focus on uniform data proposing formulae that capture both points and rectangles, and any type of object/query mobility combination (i.e., dynamic objects, dynamic queries or both). Then, we apply the model to nonuniform datasets by introducing spatio-temporal histograms, which in addition to the spatial, also consider the velocity distributions during partitioning. The advantages of our techniques are (i) high accuracy (1-2 orders of magnitude lower error than previous techniques), (ii) ability to handle all query types, and (iii) efficient handling of updates.

title: Relevance search and anomaly detection in bipartite graphs
abstract: Many real applications can be modeled using bipartite graphs, such as users vs. files in a P2P system, traders vs. stocks in a financial trading system, conferences vs. authors in a scientific publication network, and so on. We introduce two operations on bipartite graphs: 1) identifying similar nodes (relevance search), and 2) finding nodes connecting irrelevant nodes (anomaly detection). And we propose algorithms to compute the relevance score for each node using random walk with restarts and graph partitioning; we also propose algorithms to identify anomalies, using relevance scores. We evaluate the quality of relevance search based on semantics of the datasets, and we also measure the performance of the anomaly detection algorithm with manually injected anomalies. Both effectiveness and efficiency of the methods are confirmed by experiments on several real datasets.