Ontology-enabled pervasive computing applications

Information technology's rapid evolution has made tremendous amounts of information and services available at our fingertips. However, we still face the frustration of trying to do simple things in the device- and application-rich environments where we live and work. Task computing is defined as computation to fill the gap between the tasks that users want to perform and the services that constitute available actionable functionality. To support task computing, we have implemented a Task Computing Environment including client environment, service discovery mechanism, and Semantic Web services and tools. TCE is composed of several components including STEER (Semantic Task Execution EditoR), White Hole, and PIPE (Pervasive Instance Provision Environment).     

Market mechanisms for resource allocation in pervasive sensor applications

This paper describes the use of market mechanisms for resource allocation in pervasive sensor applications to maximize their Value of Information (VoI), which combines the objectively measured Quality of Information (QoI) with the subjective value assigned to it by the users. The unique challenge of pervasive sensor applications that we address is the need for adjusting resource allocation in response to the changing application requirements and evolving sensor network conditions. We use two market mechanisms: auctions at individual sensor nodes to optimize routing, and switch options to optimize dynamic selection of sensor network services as well as switching between modes of operation in pervasive security applications. We also present scenarios of transient congestion management and home security system to motivate the proposed techniques.     

Query-based association rule mining supporting user perspective

Two parameters, namely support and confidence, in association rule mining, are used to arrange association rules in either increasing or decreasing order. These two parameters are assigned values by counting the number of transactions satisfying the rule without considering user perspective. Hence, an association rule, with low values of support and confidence, but meaningful to the user, does not receive the same importance as is perceived by the user. Reflecting user perspective is of paramount importance in light of improving user satisfaction for a given recommendation system. In this paper, we propose a model and an algorithm to extract association rules, meaningful to a user, with an ad-hoc support and confidence by allowing the user to specify the importance of each transaction. In addition, we apply the characteristics of a concept lattice, a core data structure of Formal Concept Analysis (FCA) to reflect subsumption relation of association rules when assigning the priority to each rule. Finally, we describe experiment results to verify the potential and efficiency of the proposed method.     

Aspect-oriented design and implementation of context-aware pervasive applications

Pervasive applications must be aware of the contexts where they are executed. These contexts may vary greatly and change quickly. Two main problems are associated with this issue: (1) context-awareness is a crosscutting concern that cannot be well-encapsulated in a single module using traditional technologies, thus hindering software maintenance and reusability; and (2) reasoning about application design correctness can be complex due to the number and diversity of potential contexts where a pervasive application could be executed. In order to overcome these problems, we present a process for the design and implementation of context-aware pervasive applications that uses aspect-orientation and executable modelling in order to overcome these shortcomings. Aspect-oriented techniques contribute to the encapsulation of crosscutting concerns, such as context-awareness, into well-localized modules. Executable modelling helps engineers to reason about application design by executing the design models in different contexts and situations. Pervasive applications are modelled using the aspect-oriented executable modelling UML 2.0 profile, executed at the modelling level for testing purposes, and then mapped into an aspect-oriented middleware platform for pervasive applications. This process is illustrated using a location-aware intelligent transportation system consisting of a set of cooperating sentient vehicles. This work has been supported by the Spanish Ministerio de Ciencia y Tecnologa (MCYT) Project TIN2005-09405-C02-01 and the European Commission Grant IST-2-004349-NOE AOSD-Europe and the European Commission STREP Project AMPLE IST-033710.     

Unsupervised feature selection for sensor time-series in pervasive computing applications

The paper introduces an efficient feature selection approach for multivariate time-series of heterogeneous sensor data within a pervasive computing scenario. An iterative filtering procedure is devised to reduce information redundancy measured in terms of time-series cross-correlation. The algorithm is capable of identifying nonredundant sensor sources in an unsupervised fashion even in presence of a large proportion of noisy features. In particular, the proposed feature selection process does not require expert intervention to determine the number of selected features, which is a key advancement with respect to time-series filters in the literature. The characteristic of the prosed algorithm allows enriching learning systems, in pervasive computing applications, with a fully automatized feature selection mechanism which can be triggered and performed at run time during system operation. A comparative experimental analysis on real-world data from three pervasive computing applications is provided, showing that the algorithm addresses major limitations of unsupervised filters in the literature when dealing with sensor time-series. Specifically, it is presented an assessment both in terms of reduction of time-series redundancy and in terms of preservation of informative features with respect to associated supervised learning tasks.     

一种基于倒排索引树的增量更新关联挖掘算法

增量更新关联规则挖掘主要解决事务数据库中交易记录不断更新和最小支持度发生变化时关联规则的维护问题。针对目前诸多增量更新关联规则挖掘算法存在效率低、计算成本高、规则难以维护等问题,提出一种基于倒排索引树的增量更新关联挖掘算法。该算法有效地将倒排索引技术与树型结构相结合,使得交易数据库中的数据不断更新和最小支持度随应用环境不同而不断改变时,以实现无需扫描原始交易数据库和不产生候选项集的情况下生成频繁项集。实验结果表明,该算法只需占用较小的存储空间、且检索项集的效率较高,能高效地解决增量更新关联规则难以维护的问题。     

An efficient cache conscious multi-dimensional index structure

Recently, to relieve the performance degradation caused by the bottleneck between CPU and main memory, cache conscious multi-dimensional index structures have been proposed. The ultimate goal of them is to reduce the space for entries so as to widen index trees and minimize the number of cache misses. The existing index structures can be classified into two approaches according to their entry reduction methods. One approach is to compress MBR keys by quantizing coordinate values to the fixed number of bits. The other approach is to store only the sides of minimum bounding regions (MBRs) that are different from their parents partially. The second approach works well when the size of a node is small and the number of entries is small. In this paper, we investigate the existing multi-dimensional index structures for main memory database systems through experiments under the various work loads. Then, we propose a new index structure that exploits the properties of the both techniques. We implement existing multi-dimensional index structures and the proposed index structure. We perform various experiments to show that our approach outperforms others.     

The MPE toolkit for supporting distributed applications

This paper presents a toolkit for supporting the execution of coarse-grain, parallel (distributed) applications under the MOSIX multicomputer operating system. These tools use standard UNIX System V process control and message-passing facilities, as well as the dynamic process migration mechanisms of MOSIX. The MPE tools can be used to modify sequential applications that were originally written for execution in a uniprocessor environment, to run efficiently in a distributed environment, consisting of several loosely coupled independent computers that communicate by messages. After presenting the MPE tools, the paper gives examples of several sequential algorithms that have been modified for execution in such a distributed multicomputer, as well as the resulting execution speed-ups that were obtained.     

An OSGi-based flexible and adaptive pervasive cloud infrastructure

There is a trend that different computing paradigms such as cloud computing, pervasive, and mobile computing are converging with each other. Due to this convergence, there arise unprecedented complexities, such as huge number of computing devices, flexibilities and adaptation of service infrastructure （infrastructure elasticity） for fitting dynamics of large smart city applications, expectations of powerful computing and storage capabilities on handhold devices, and so on. Therefore, a supporting infrastructure is needed, which can flexibly switch services at run time and can be used to enhance capabilities for small devices through component/service migrations. In this paper, we propose an elastic open service gateway initiative （OSGi）-based pervasive cloud （OSGi-PC） infrastructure which can make use of both the cloud computing capabilities and the component flexibilities from OSCi. OSGi-PC provides flexible management of component migrations between small devices themselves and powerful nodes in between, which is remaining a critical challenge for enabling mobile clouds. We have evaluated the OSGi-PC in terms of performance for adaptive service provision, and power consumption during service adaptation, performance and power consumption for component migrations in different scenarios, which show the usability of OSGi-PC.     

An effective and efficient approximate two-dimensional dynamic programming algorithm for supporting advanced computer vision applications

Dynamic programming is a popular optimization technique, developed in the 60’s and still widely used today in several fields for its ability to find global optimum. Dynamic Programming Algorithms (DPAs) can be developed in many dimension. However, it is known that if the DPA dimension is greater or equal to two, the algorithm is an NP complete problem. In this paper we present an approximation of the fully two-dimensional DPA (2D-DPA) with polynomial complexity. Then, we describe an implementation of the algorithm on a recent parallel device based on CUDA architecture. We show that our parallel implementation presents a speed-up of about 25 with respect to a sequential implementation on an Intel I7 CPU. In particular, our system allows a speed of about ten 2D-DPA executions per second for 85 × 85 pixels images. Experiments and case studies support our thesis.     

The TV-tree: An index structure for high-dimensional data

We propose a file structure to index high-dimensionality data, which are typically points in some feature space. The idea is to use only a few of the features, using additional features only when the additional discriminatory power is absolutely necessary. We present in detail the design of our tree structure and the associated algorithms that handle such varying length feature vectors. Finally, we report simulation results, comparing the proposed structure with theR ^*-tree, which is one of the most successful methods for low-dimensionality spaces.The results illustrate the superiority of our method, which saves up to 80% in disk accesses.     

Supporting pervasive computing applications with active context fusion and semantic context delivery

Future pervasive computing applications are envisioned to adapt the applications’ behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.     

Supporting rapid design and evaluation of pervasive applications: challenges and solutions

Along with the improvement of the key technologies for pervasive computing, the design of applications itself has emerged as a notable research area. A design should decide which features go into the application of supporting its context-awareness and ascertaining how well those features add value for users with an acceptable user experience. This paper initially identifies the challenges in designing and evaluating pervasive applications. It then presents a condensed survey of prototyping techniques and existing toolkits with the aim of understanding how the design issues have been addressed by research prototypes. At the end, we describe various open issues with suggestions of possible ways to extend the capabilities of current prototyping toolkits.     

A comprehensive approach to model and use context for adapting applications in pervasive environments

With an increasing diversity of pervasive computing devices integrated in our surroundings and an increasing mobility of users, it will be important for computer systems and applications to be context-aware. Lots of works have already been done in this direction on how to capture context data and how to carry it to the application. Among the remaining challenges are to create the intelligence to analyze the context information and deduce the meaning out of it, and to integrate it into adaptable applications. Our work focuses on these challenges by defining generic context storage and processing model and by studying its impact on the application core. We propose a reusable context ontology model that is based on two levels: a generic level and a domain specific level. We propose a generic adaptation framework to guarantee adaptation of applications to the context in a pervasive computing environment. We also introduce a comprehensive adaptation approach that involves content adaptation and presentation adaptation inline with the adaptation of the core services of applications. Our case study shows that the context model and the application adaptation strategies provide promising service architecture.     

一种支持高效并行处理的矢量数据索引方法

分析了HBase的存储模型和Spark的并行处理机制,提出一种矢量空间数据的分布式存储、索引和并行区域查询方法。设计了基于空间对象中心点的行键存储方案,将中心点的Hilbert编码与经纬度小数位结合实现行键的唯一性,保证地理位置接近的要素在表中存储在相邻的行。实现了基于Spark的空间索引并行构建和区域查询方法,借助空间对象中心点的Hilbert编码快速构建索引,通过多边形区域的最小外接矩形过滤查询结果。实验结果表明,索引并行构建可靠性好速度快,区域查询并行处理算法可行且效率高。