2503641
Research on Simulation Service Ontology and Its Matchmaking Model
Web Service is the key supporting technology for large scale resource sharing and application integration. Service-Oriented distributed simulation is becoming a very hot research topic in M&S. Aiming at the problem of simulation model service description and discovery, semantics-oriented simulation service ontology and description models are presented in this paper. A matchmaking model named ldquofour types and three levelsrdquo is proposed for simulation services discovery. Similarity functions are introduced to rank and select the simulation services. In particularly, some key matching algorithms are studied.
15819388
High level architecture evolved modular federation object model
To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic Federation Object Model (FOM), a modular FOM was proposed by High Level Architecture (HLA) Evolved product development group. This paper reviews the state-of-the-art of HLA Evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and Base Object Model (BOM) is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate composable simulation both in academia and practice is presented and future directions are pointed out.
20747285
A transformation model from DEVS to SMP2 based on MDA
13485709
Three-dimensional conceptual model for service-oriented simulation
In this letter, we propose a novel three-dimensional conceptual model for an emerging service-oriented simulation paradigm. The model can be used as a guideline or an analytic means to find the potential and possible future directions of the current simulation frameworks. In particular, the model inspects the crossover between the disciplines of modeling and simulation, service-orientation, and software/systems engineering. Finally, two specific simulation frameworks are studied as examples.
22669776
Identities via Bell matrix and Fibonacci matrix
In this paper, we study the relations between the Bell matrix and the Fibonacci matrix, which provide a unified approach to some lower triangular matrices, such as the Stirling matrices of both kinds, the Lah matrix, and the generalized Pascal matrix. To make the results more general, the discussion is also extended to the generalized Fibonacci numbers and the corresponding matrix. Moreover, based on the matrix representations, various identities are derived.
2987996
Mining patterns in long sequential data with noise
Pattern discovery in time series data has been a problem of great importance in many fields, e.g., computational biology, performance analysis, consumer behavior, etc. Recently, considerable amount of research has been carried out in this area. The facts that the input data is typically very large and noises may present in various formats pose great challenge to the mining process. Recently, we have made several new research advances in this area. In this paper, we present some of them. We will survey new models proposed to address different types of noises as well as scalable algorithms developed for efficiently mining patterns under each model.
13205970
Performance analysis of several algorithms for processing joins between textual attributes
Three algorithms for processing joins on attributes of a textual type are presented and analyzed in this paper. Since such joins often involve document collections of very large size, it is very important to find efficient algorithms to process them. The three algorithms differ according to whether the documents themselves or the inverted files on the documents are used to process the join. Our analysis and simulation results indicate that the relative performance of these algorithms depends on the input document collections, the system characteristics and the input query. For each algorithm, the type of input document collection with which the algorithm is likely to perform well is identified.
7046093
Adaptive contact probing mechanisms for delay tolerant applications
In many delay tolerant applications, information is opportunistically exchanged between mobile devices who encounter each other. In order to effect such information exchange, mobile devices must have knowledge of other devices in their vicinity. We consider scenarios in which there is no infrastructure and devices must probe their environment to discover other devices. This can be an extremely energy consuming process and highlights the need for energy conscious contact probing mechanisms. If devices probe very infrequently, they might miss many of their contacts. On the other hand, frequent contact probing might be energy inefficient. In this paper, we investigate the trade-off between the probability of missing a contact and the contact probing frequency. First, via theoretical analysis, we characterize the trade-off between the probability of a missed contact and the contact probing interval for stationary processes. Next, for time varying contact arrival rates, we provide an optimization framework to compute the optimal contact probing interval as a function of the arrival rate. We characterize real world contact patterns via Bluetooth phone contact logging experiments and show that the contact arrival process is self-similar. We design STAR, a contact probing algorithm which adapts to the contact arrival process. Via trace driven simulations on our experimental data, we show that STAR consumes three times less energy when compared to a constant contact probing interval scheme.
6000340
Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data
Uncertain data are inherent in various important applications and reverse nearest neighbor (RNN) query is an important query type for many applications. While many different types of queries have been studied on uncertain data, there is no previous work on answering RNN queries on uncertain data. In this paper, we formalize probabilistic reverse nearest neighbor query that is to retrieve the objects from the uncertain data that have higher probability than a given threshold to be the RNN of an uncertain query object. We develop an efficient algorithm based on various novel pruning approaches that solves the probabilistic RNN queries on multidimensional uncertain data. The experimental results demonstrate that our algorithm is even more efficient than a sampling-based approximate algorithm for most of the cases and is highly scalable.