This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped and implemented within the scope of the PERCEPOLIS cyber-infrastructure and its effectiveness has been verified on a testbed of real student records for select academic programs.

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student’s interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

There has been a prolific rise in the popularity of cloud storage in recent years. While cloud storage offers many advantages such as flexibility and convenience, users are typically unable to tell or control the actual locations of their data. This limitation may affect users' confidence and trust in the storage provider, or even render cloud unsuitable for storing data with strict location requirements. To address this issue, we propose a system called LAST-HDFS which integrates Location-Aware Storage Technique (LAST) into the open source Hadoop Distributed File System (HDFS). The LAST-HDFS system enforces location-aware file allocations and continuously monitors file transfers to detect potentially illegal transfers in the cloud. Illegal transfers here refer to attempts to move sensitive data outside the (“legal”) boundaries specified by the file owner and its policies. Our underlying algorithms model file transfers among nodes as a weighted graph, and maximize the probability of storing data items of similar privacy preferences in the same region. We equip each cloud node with a socket monitor that is capable of monitoring the real-time communication among cloud nodes. Based on the real-time data transfer information captured by the socket monitors, our system calculates the probability of a given transfer to be illegal. We have implemented our proposed framework and carried out an extensive experimental evaluation in a large-scale real cloud environment to demonstrate the effectiveness and efficiency of our proposed system.

The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

A smart grid is a cyber-physical system designed to achieve sustainability by facilitating the use of renewable energy sources, without compromising the reliability of power distribution and transmission. The research presented in this paper seeks to facilitate analysis of the reliability achieved by smart grids. To this end, we present an analytical reliability model that captures the effect of impairments originating from both physical and cyber components, as well as the effect of cyber-physical interdependencies among these components. The model can be instantiated with failure data from field use or simulation. We categorize and quantify dependencies in the smart grid and analyze the impact on reliability of introducing additional interdependencies. Finally, we present a case study that investigates the effect of physical and cyber improvements on overall reliability of the smart grid, and demonstrate that flawed cyber infrastructure can result in reliability lower than that of a conventional power grid with less advanced control.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

There has been a prolific rise in the popularityof cloud storage in recent years. While cloud storage offersmany advantages such as flexibility and convenience, users arenow unable to tell or control the actual locations of their data. This limitation may affect users' confidence and trust in thestorage provider, or even be unsuitable for storing data withstrict location requirements. To address this issue, we proposean illegal file transfer detection framework that constantlymonitors the real-time file transfers in the cloud and is capableof detecting potential illegal transfers which moves sensitivedata outside the ("legal") boundaries specified by the fileowner. The main idea is to classifying multiple users' location preferences when making the data storage arrangement inthe cloud nodes. We model the legal file transfers amongnodes as a weighted graph and then maximize the probabilityof storing data items of similar privacy preferences in thesame region. Then we leverage the socket monitoring functionsprovided by LAST-HDFS (a recent location-aware Hadoop filestorage system) to monitor the real-time communication amongcloud nodes. Based on our legal file transfer graph and thedetected communication, we propose an approach to calculatethe probability of the detected transfer to be illegal.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Today, we see ICT everywhere in our life. ICT has been continuously finding new application fields to enrich our quality of life. This also implies that ICT has been continuously finding new challenges that promotes deeper understanding of computing. New application fields would include green computing, e-Health, mobile computing and big data analysis. To solve the problems in these fields, we are seeing substantial deepening in computing technologies such as supercomputing and machine learning. NATA is designed as a forum where novel applications and technology advances in computing meet. At this panel of the first symposium, we would like to share the state of the art of their interactions. Four specialists of green computing, service centric networking, e-health and machine learning will meet, show the points from their views, and exchange ideas.

Presents a listing of the Symposium organizing committee.

We are experiencing the expanding use of location-based services such as AT&T’s TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which has great potential for statistical usage in applications like traffic flow analysis, infrastructure planning, and advertisement dissemination. The key challenge is how to wisely use the data without violating each user’s location privacy concerns. In this article, we first identify a new privacy problem, namely, the inference-route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have demonstrated that our approach outperforms the latest related work in terms of both efficiency and effectiveness.

This paper describes Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support (PERCEPOLIS), where context-aware recommendation algorithms facilitate personalized learning and instruction. Fundamental to PERCEPOLIS are (a) modular course development and offering, which increase the resolution of the curriculum and allow for finer-grained personalization of learning artifacts and associated data collection; (b) blended learning, which allows class time to be used for active learning, interactive problem solving and reflective instructional tasks; and (c) networked curricula, in which the components form a cohesive and strongly interconnected whole where learning in one area reinforces and supports learning in other areas. Intelligent software agents customize the content of a course for each learner, based on his or her academic profile and interests, aided by context-based recommendation algorithms. This paper provides an introduction to the PERCEPOLIS platform, with focus on these algorithms; and describes the educational research that underpins its design.

Based on the principle of divide and conquer, in this paper we propose an efficient traceback protocol for mobile ad hoc networks, The protocol is capable of detecting a hotspot where the attacker resides. It works by dividing the forwarding path of every packet into multiple interweaving fragments and each reachable fragment is individually reconstructed during a traceback process. Through simulations in theoretical mobility models as well as real mobility traces, we show that each traceback of our scheme can attribute to a very small hotspot and the attacker can be accurately identified after a number of traceback operations.

Sensor network has given rise to a comprehensive view of various environments through their harnessed data. Usage of the data is, however, limited since the current sensor network paradigm promotes isolated networks that are statically tasked. In recent years, users have become mobile entities that require constant access to data for efficient processing. Under the current limitations of sensor networks, users would be restricted to using only a subset of the vast amount of data being collected, depending on the networks they are able to access. Through reliance on isolated networks, proliferation of sensor nodes can easily occur in any area that has high appeals to users. Furthermore, support for dynamic tasking of nodes and efficient processing of data is contrary to the general view of sensor networks as subject to severe resource constraints. Addressing the aforementioned challenges requires the deployment of a system that allows users to take full advantage of data collected in the area of interest to their tasks. In light of these observations, we introduce a hardware-overlay system designed to allow users to efficiently collect and utilize data from various heterogeneous sensor networks. The hardware-overlay takes advantage of FPGA devices and the mobile agent paradigm in order to efficiently collect and process data from cooperating networks. The computational and power efficiency of the system are herein demonstrated.

In this research, we propose a robust Sybil attack detection framework for MANETs based on cooperative monitoring of network activities. We do not require designated and honest monitors to perform the Sybil attack detection. Each mobile node in the network observes packets passing through it and periodically exchanges its observations in order to determine the presence of an attack. Malicious nodes fabricating false observations will be detected and rendered ineffective. Our framework requires no centralized authority and, thus, is scalable in expanding network size. Privacy of each mobile node is also a consideration of our framework. Our preliminary experimental results yield above 80% accuracy (true positives) and about 10% error rate (false positives).

Power saving is a critical issue in wireless ad hoc networks. Many of the existing energy efficient routing protocols utilize the power management feature provided by MAC layer (e.g. IEEE 802.11 PSM). The rationale behind these protocols is to power down (put in sleep) the redundant nodes that do not involve in the packets routing. To keep a low end-to-end latency and preserve the routing fidelity, a group of nodes are selected to constitute a virtual backbone throughout the network. The backbone nodes do not sleep all the time serving for the packets routing while the rest of nodes turn to sleep mode if there is no packet to send or receive. Backbone nodes are periodically selected and replaced; the current selection and replacement mechanism mainly concerns the energy balance and fairness among the network nodes while the security threat is not considered. In this paper, we present that the adversary may launch security attacks against the network by exploiting the vulnerabilities of the backbone node selection algorithm. To address this problem, we propose a biased random selection algorithm to prevent the security attacks. The evaluation results indicate that with the aid of reputation system, the proposed biased random selection scheme is more secure than the random selection scheme while trading for a limited amount of power efficiency.

Pervasive computing enables seamless integration of computing technology into everyday life to make up-to-date information and services proactively available to the users based on their needs and behaviors. We aim to develop a transaction management scheme as a pertinent component for such environment supported by either structured or ad hoc networks. We propose transactional agents for pervasive computing (TAPCO), which utilizes a dynamic hierarchical meta data structure that captures the semantic contents of the underlying heterogeneous data sources. Mobile agents process the transactions collaboratively, to preserve ACID properties without violating local autonomy of the data sources. TAPCO is simulated and compared against decentralized serialization graph testing (DSGT) protocol. The results show that TAPCO outperforms DSGT in several ways. In contrast to DSGT that did not consider local transactions, TAPCO supports both local and global transactions without violating the local autonomy.

Semantic caching was originally used for structural data objects such as 2D location data and cannot be directly applied to mobile image data access. First, traditional semantic caching relies on exact match and therefore is not suitable for approximate and similarity-based queries. Second, the semantic description of cached data is defined on query context instead of data content, which leads to inefficient use of cache storage. Third, the semantic description of cached data does not reflect the popularity of the data, making it difficult to conduct popularity-driven content analysis and prediction. To facilitate content-based image retrieval in wireless ad hoc networks, we propose a semantic-aware image caching (SAIC) scheme in this paper. The proposed scheme can efficiently utilize the cache space and significantly reduce the cost of image retrieval. The proposed SAIC scheme is based on several innovative ideas: 1) multilevel representation of the semantic contents, 2) association-based and Bayesian probability-based content prediction, 3) constraint-based representation method showing the semantic similarity between images, 4) nonflooding query processing, and 5) adaptive cache consistency maintenance. The proposed model is introduced, and through extensive simulation, its behavior has been compared against two state-of-the-art ad hoc caching schemes as advanced in the literature.

The growing application of caching in Internet applications have heretofore relied largely on qualitative observation and empirical data on the update behavior of Internet data in their design. While it is empirically known that the update behavior of such data is distinctly bimodal, much less is known about the details of these behaviors and the processes that drives them. A detailed study of the composition of modern Web sites that includes in-depth analyses of the changes made to the data of which those sites are composed offers a large potential benefit not only to Internet caching protocol developers but also to a broad cross-section of information technology professionals and researchers. The first results of just such a study are reported in this paper. A variety of popular Internet Web sites were selected and monitored for changes in both composition and content over a period of several weeks. Data collected in this study is then analyzed using traditional stochastic methods. The results of this investigation are summarized and suggested research directions conclude this article

In this paper, we propose a new term weighting scheme called term frequency-inverse corpus frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application, unsupervised document clustering, we evaluated the effectiveness of the proposed approach in comparison to five widely used term weighting schemes through extensive experimentation. Our results show that TF-ICF can produce document clusters that are of comparable quality as those generated by the widely recognized term weighting schemes and it is significantly faster than those methods

A mobile device retrieving objects from parallel air channels has to optimize multiple objectives: (1) reduce the response time for object retrieval, and (2) reduce power consumption in the device. This multi-objective optimization becomes even more difficult when the same object is broadcast multiple times over the parallel air channels to increase availability. In this work, several heuristic algorithms - branch and bound, greedy, random, and first-choice have been proposed for efficient object retrieval from parallel air channels in the presence of replicated objects. Empirical evaluation of the algorithms shows that the greedy algorithm produces near-optimal-response time with minimum overhead in terms of energy consumption. The runtime of the greedy algorithm is significantly faster than that of the optimal branch and bound algorithm, and is similar to the firstchoice and random algorithms.

A location dependent query (LDQ) result set is valid only in a specific region called the validity region (VR). While limiting the validity of a particular result set to a given area, the VR may also be used in caching implementations to determine if cached results satisfy semantically equivalent queries. Existing LDQ caching schemes rely on the database servers to provide the VR at a cost of high computational overhead. Alternatively, a LDQ proxy cache, which approximates the VR can be employed, freeing the database servers from the high cost of calculating the VR. A LDQ proxy cache architecture is proposed to compute an estimated validity region (EVR) based on the observed querying history at the proxy server. We present an algorithm - Window_EVR - for the LDQ proxy to compute the EVR for a window query result set. The simulation results show that LDQ proxy caching using the Window_EVR algorithm significantly reduces both the window query response time and the workload at the database servers while maintaining query result set accuracy.

Emerging applications of mobile data management, such as content-based image retrieval in ad hoc networks, require the awareness of content distribution to enact and optimize the communication and search tasks. Caching is a widely used approach in mobile environments to improve system performance and keep track of replications. Within the scope of mobile and ubiquitous computing infrastructure, traditional caching techniques are not effective for large-size data such as images due to the limitations of bandwidth, storage, and power. The functionality of caching techniques relies on exact match, making them unsuitable for imprecise and similarity-based queries. In addition, the description of cached contents is defined based on the query context instead of data content, which fails to exploit the semantic locality of cached data and makes the traditional caching techniques inefficient in utilizing cache storage. In this paper, we present a semantic-aware caching scheme (SAIC) for image databases in ad hoc networks. The proposed scheme is designed based on several novel ideas: 1) multi-level partitioning of semantic space, 2) constraint-based representation of image semantics, 3) non-flooding query resolution, and 4) adaptive cache consistency maintenance. Our combination of theoretical analysis and simulation show the efficiency of the proposed scheme, and evaluate its performance in comparison against two ad hoc caching schemes as advanced in the literature.

Spatial query processing is important to the emerging mobile network applications such as ad hoc networks and mobile sensor networks. The conventional caching techniques are ineffective in dealing with spatial queries in dynamic environments due to the following reasons: first, the cache contents are described based on queries instead of data locations, which lead to the inefficiency in describing the data content distribution in the network; in addition, the results of earlier queries may be invalidated immediately after query resolution due to the mobility of data objects; moreover, the data volumes and number of clients may be quite large that deteriorate the performance of conventional caching approaches. In this paper, we address these issues using position-locus map, a caching scheme that reflects the distribution of mobile data objects based on the analysis of locations of interest and earlier queries. We show that spatial queries may be answered efficiently with the position-locus map, with reduced search cost and system overhead

Recent years saw the rapid development of peer-to-peer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the technological constraints of P2P networks. However, traditional caching techniques have some major shortcomings that make them unsuitable for similarity search, such as the lack of semantic locality representation and the rigidness of exact matching on data objects. To facilitate the efficient similarity search, we propose semantic-aware caching scheme (SAC) in this paper. The proposed scheme is hierarchy-free, fully dynamic, non-flooding, and do not add much system overhead. By exploring the content distribution, SAC drastically reduces the cost of similarity-based k-NN retrieval in P2P networks. The performance of SAC is evaluated through simulation study and compared against several search schemes as advanced in the literature.

Mobile agent technology has been used in wireless distributed information retrieval for many years. However, the energy-efficiency issue in such systems remains unexplored. In this paper, we proposed an adaptive application-driven power management (AADPM) protocol for mobile agent-based applications within the IEEE 802.11b infrastructure WLAN environment. Our goal is to minimize energy while achieving low round trip time (RTT) delay. We discuss the protocol and evaluate its effectiveness using the network simulator NS2. We also draw horizontal comparisons among a variety of PM methods reported in the literature. Experimental results show that, compared to the power save mode supported by 802.11b, AADPM reduces the network interface card energy consumption by 65% while only introducing 3% RTT delay.

Content-based image retrieval is a challenging problem in mobile ad hoc networks due to the multiple limitations such as network bandwidth, infrastructure-free nature, and node mobility. The traditional systems employ either centralized or flooding strategies, which may result in low fault tolerance or high search cost. In this paper, we propose a decentralized non-flooding retrieval scheme in multi-hop ad hoc networks - semantic ad hoc image retrieval (SAIR). The novelty of SAIR stems from several factors including: (1) representation of image contents using first-order logic expressions; (2) clustering mobile nodes based on their data contents; and (3) performing content-based image retrieval within a reduced scope of mobile nodes. Through extensive simulations, we show that relative to the flooding strategy, SAIR can retrieve the semantically most similar image objects by accessing only a small portion of the mobile nodes with lower search cost. Moreover, it is scalable to large network sizes and large number of data objects.

Mobile ad hoc networks have multiple limitations in performing similarity-based nearest neighbor search - dynamic topology, frequent disconnections, limited power, and restricted bandwidth. Cooperative caching is an effective technique to reduce network traffic and increase accessibility. In this paper, we propose to solve the k-nearest-neighbor search problem in ad hoc networks using a semantic-based caching scheme which reflects the content distribution in the network. The proposed scheme describes the semantic similarity among data objects using constraints, and employs cooperative caching to estimate the content distribution in the network. The query resolution based on the cooperative caching scheme is non-flooding and hierarchy-free.

In this paper we present an adaptive application-driven power management (AADPM) strategy with online idle period length distribution learning capability for the IEEE 802.11b WLAN. We discuss its design and evaluate the performance in comparison with other power management strategies using the network simulator NS2. We simulated both the single user and multiple users scenarios. Experimental results have shown that, compared with other power management methods examined in this paper, AADPM achieved the highest energy saving in all cases and demonstrated strong adaptability to network congestion.

Ad hoc networks build temporary wireless connections in environments where the stationary infrastructures are either destroyed or too expensive to construct. Most of the previous research in ad hoc networks focuses on routing protocols that adapt to the dynamic network topologies, and not much work has been done on data accessing. One important data accessing application is similarity search, which provides the foundation of content-based retrieval. Many traditional similarity search algorithms are based on centralized or flooding mechanisms, which are not effective in wireless ad hoc network environments due to the multiple limitations such as bandwidth and power. In this paper we tackle the problem of similarity search by using semantic-based caching to reflect the data content distribution in the network. The basic idea is analyzing the cached results of earlier queries and trying to resolve the later queries within a small collection of content-related mobile nodes. Based on a Hilbert space-filling curve, the data points in a multi-dimensional semantic space are described as a linear representation. These data points are further cached to facilitate query processing. Through extensive simulations, we show that our method can perform similarity search with improved performance in terms of search cost and response time.

The infrastructure-free and self-organizing nature of wireless ad hoc networks presents fundamental challenges to the design of content-based multimedia search algorithms that are efficient with respect to search cost and fair across various network setups. In contrast to the wealth of research literature on ad hoc routing protocols, few works have realistically considered the methods of locating multimedia data sources in a highly dynamic ad hoc network. Moreover, multimedia information retrieval strategies proposed in the wired networks are not applicable in the context of wireless ad hoc networks, due to the limitations of bandwidth and energy. In this paper, we describe two probability-based schemes for the efficient multimedia content location in wireless ad hoc networks. The Association-based Content Prediction (ACP) scheme and Bayesian-based Content Prediction (BCP) scheme make use of probabilistic information to lower proactive network traffic while minimizing search cost. The combination of theoretical analysis and simulation results shows that the proposed schemes perform favorably in terms of response time, traffic complexity, and scalability.

Mobile ad hoc networks have attracted more and more research attention by providing of wireless communications without location limitations and pre-built infrastructures. Due to the lack of static supporting structures, ad hoc networks are prone to several limitations, such as bandwidth, connectivity and power. Multimedia retrieval is a challenging task in ad hoc networks due to the multiple limitations. To provide an efficient platform for multimedia retrieval, we propose to cluster multimedia data based on their semantic content, then construct a virtual hierarchical infrastructure overlaid on the ad hoc network. The rationale of semantic-based clustering is discussed. The effectiveness of the proposed scheme was investigated through theoretical analysis and simulation.

Multidatabase systems are widely used to integrate distributed heterogeneous data sources. To support mobile data access, existing multidatabases must address the obstacles in mobile computing such as low bandwidth, intermittent network connections, and resource constraints of mobile devices. The execution autonomy of mobile agents alleviates these problems and provides opportunity for conserving energy. We proposed and implemented a novel multidatabase information retrieval system, MAMDAS, which represents an application of Mobile Agents within the Mobile Data Access System framework. In this paper, we propose several MAMDAS optimization techniques to improve its performance and study the effect of mobile agents on energy saving through extensive simulation. Our experimental results show that by deploying appropriate power management policies, the mobile device energy saving can achieve as high as 51%. The high performance, energy efficiency, scalability, and robustness of MAMDAS allow us to envision its application in a variety of fields such as military operations, emergency teams, and mobile e-business.

Caching has long been employed in computer system architectures to improve performance in terms of reduced memory access times and latencies and hence, to improve throughput at the expense of additional complexity in memory organization and of managing multiple copies of shared data. In traditional large-scale computer architectures, the cache coherency schemes developed permit very large, cache-coherent non-uniform memory access (CC-NUMA) to shared memory spaces. However, these approaches fail when applied to the vastness of the Internet and the growing complexities introduced by mobility. While caching has been successfully employed in specific, limited applications in modern Internet implementations, there has been no general-purpose approach to cache coherency on the Internet. A quality-of-service (QOS) approach is ideally suited for such a general-purpose cache coherence protocol, providing strong consistency for those data items that require it while permitting weaker consistency for less critical data. A statistical analysis of the read/write behavior of typical Internet data is used to suggest a low overhead, inexpensive QOS solution to cache coherency issues on the Internet. An experimental framework studies and verifies the potential of the proposed scheme as a viable solution to cache coherence on both wired and wireless Internet applications.

With the goal of providing "timely and reliable" access to information in a mobile computing environment, mobile units and the wireless medium operate under constraints on energy, bandwidth, and connectivity. Among these limitations, power limitation of mobile units is one of the key issues. In a mobile computing environment, broadcasting has proved to be an effective method to distribute public data. Efficient methods for allocating and retrieving objects on parallel indexed broadcast channels have been proposed to manage power consumption and access latency. Employment of parallel channels also brings out the notion of conflicts. To minimize the effect of conflicts on both access latency and power consumption, one has to develop schemes to schedule access to the objects that minimizes the number of passes over the parallel channels. This work extends our past efforts and proposes two new scheduling algorithms that can find the minimum number of passes and inside channel switches. The simulation results show that the proposed scheduling algorithms relative to our previous work have a great impact on energy consumption and access latency. The proposed scheduling algorithms are simulated and results are presented.

Creating a global information-sharing environment in the presence of autonomy and heterogeneity of data sources is a difficult task. When adding mobility and wireless medium to this mix, the constraints on bandwidth, connectivity, and resources worsen the problem. Our past research in global information sharing systems resulted in the design, implementation, and prototype of a search engine, the summary schemas model, which supports imprecise global accesses to the data sources while preserving local autonomy. We extended the scope of our search engine by incorporating mobile agent technology. The use of mobile agents alleviates many problems associated with wireless communication. We devised and implemented the MAMDAS system, which applies mobile agent technology in a mobile data access system framework. As shown by our experimental study, MAMDAS achieved better performance, scalability, portability, and robustness compared to its predecessors.

Security issues in multidatabases are complicated due to autonomy and heterogeneity of local databases. Deriving global authorizations by integrating underlying local authorizations is difficult since subjects and objects at each local database may not be compatible. In addition, local authorizations may conflict and could not be combined to form common global authorizations. This paper proposes an authorization model for a multidatabase system. The summary schemas model (SSM) is used as the underlying paradigm. The SSM resolves name differences in multidatabases using word relationships defined in a standard dictionary. Hypernyms and hyponyms of access terms exported from local databases are the main components of the SSM as they form a hierarchical metadata structure. SSM global authorizations tagged to hypernyms are derived from local authorizations using global roles and a role hierarchy defined in multidatabases. The model considers roles as common global subjects onto which local subjects can be mapped. Since the mapping can be done independently and autonomously among local databases, authorization autonomy is preserved. The paper also evaluates the performance of the proposed model. The simulation results show that the proposed model offers better performance than the original SSM since user queries with insufficient authority are rejected earlier. This results in less communication and less query response time.

The paper introduces a new approach to acceleration of nonnumeric, database, and information retrieval operations. While traditional techniques accelerate the most time-critical high-level software constructs, we propose novel low-level primitives and demonstrate how these primitives improve database operations. Radix sorting, hashing, and bit-vector operations are used to develop a new class of nonnumeric algorithms - OTHER (Ordered Table Hashing and Radix sort algorithms) - based on low-level hashing operations Init, Mark, and Scan. We have proposed and evaluated two hardware accelerators for OTHER algorithms. It is shown that a low complexity hardware support (less than 10 K transistors) can significantly improve the performance of nonnumeric operations.

As technological advances are made in software and hardware, the feasibility of accessing information "any time, anywhere" is becoming a reality. Furthermore, the diversity and amount of information available to a given user is increasing at a rapid rate. In a mobile computing environment, a potentially large number of users may simultaneously access the global data; therefore, there is a need to provide a means to allow concurrent management of transactions. Current multidatabase concurrency control schemes do not address the limited bandwidth and frequent disconnection associated with wireless networks. This paper proposes a new hierarchical concurrency control algorithm. The proposed concurrency control algorithm, v-lock, uses global locking tables created with semantic information contained within the hierarchy. The locking tables are used to serialize global transactions, detect and remove global deadlocks. Additionally, data replication, at the mobile unit, is used to limit the effects of the restrictions imposed by a mobile environment. The replicated data provides additional availability in case of a weak connection or disconnection. Current research has concentrated on page and file-based caching or replication schemes to address the availability and consistency issues in a mobile environment. In a mobile, multidatabase environment, local autonomy restrictions prevent the use of a page or file-based data replication scheme. This paper proposes a new data replication scheme to address the limited bandwidth and local autonomy restrictions. Queries and the associated data are cached at the mobile unit as a complete object. Consistency is maintained by using a parity-based invalidation scheme. A simple prefetching scheme is used in conjunction with caching to further improve the effectiveness of the proposed scheme. Finally, a simulator was developed to evaluate the performance of the proposed algorithms. The simulation results are presented and discussed.

HIS special issue of the IEEE Transactions on Computers is the result of the Parallel Architecture and Compilation Techniques (PACT2000) Conference which was successfully held in Philadelphia, Pennsylvania, during the third week of October 2000. To follow the PACT tradition, this special issue is also intended to act as a forum that brings together research in parallel architectures and compilation. As advances in technology increase processing power and computing speed, the need for new architectures and compilers to harness such a potential also increases. This is exactly the goal of this special issue in providing a forum for computer architects and compiler designers to respond to the ever increasing need for higher performance. The major theme of the special issue was composed of 13 topics ranging from performance characterization and analytical modeling, prediction/speculation mechanisms, novel architectures, and software and hardware compilation techniques. We received 115 papers and five referees reviewed each paper, on average. The program committee met on 24 and 25 June 24 2000 in Pittsburgh. A great effort was given to select high quality of papers for presentation during the conference. After a long debate about each individual paper, the program committee recommended about 15 percent of the total papers for this special issue. Based on such a recommendation, authors of these papers were contacted and encouraged to revise and expand their papers for possible inclusion in this special issue. However, to be fair and to increase the quality of this special issue, a general call for papers was also issued. The 25 papers submitted for this special issue were evaluated and refereed by another set of reviewers. Late January of 2001, the guest editors spent a great deal of time making the final decision about the papers in this special issue. It was a challenging and rewarding experience for us to select the best among the best. This effort resulted in the selection of eight papers (six regular and two brief) in this special issue.

As the performance gap between processor and memory grows, memory latency becomes a major bottleneck in achieving high processor utilization. Multithreading has emerged as one of the most promising and exciting techniques used to tolerate memory latency by exploiting thread-level parallelism. The question, however, remains as to how effective multithreading is on tolerating memory latency. The performance of multithreading is not only affected by the overlapping of memory latency with useful computation, but also strongly depends on the cache behavior and the overhead of multithreading (e.g., thread management and context-switch costs). In particular, multithreading affects the behavior of caches, and, thus, the overall performance in a nontrivial fashion. To study these issues, this paper presents the Multithreaded Virtual Processor (MVP) model. MVP integrates the multithreaded programming paradigm and a modern superscalar processor with support for fast context switching and thread scheduling. Our studies with MVP show that, in general, the performance improvements are obtained not only by tolerating memory latency but also lower cache miss rates due to exploitation of data locality. However, multithreading creates an additional stress on the memory hierarchy caused by the interference among threads. Also, the dynamic behavior of multithreaded execution hinders the instruction locality that results in a high number of misses in the L1 instruction cache.

As technological advances are made in software and hardware, the feasibility of accessing information "anytime, anywhere" is becoming a reality. There are similarities involved in accessing information in a multidatabase environment and a mobile computing environment. We have discussed the characteristics of a wireless-mobile computing environment superimposed on a multidatabase system to realize a system capable of effectively accessing a large amount of data over a wireless medium. In order to limit the effects of the restrictions imposed by a mobile environment, data duplication is used at the mobile unit. The duplicated data at the mobile node provide additional availability in case of a weak connection or disconnection. In such an environment, it is important to retain the consistency between the data residing on fixed nodes and mobile nodes. Current research has concentrated on page- and file-based caching or replication schemes to address the availability and consistency issues in a mobile environment. In a mobile multidatabase environment, local autonomy restrictions prevent the use of a page- or file-based data duplication scheme. We propose a new data duplication scheme to address the limited bandwidth and local autonomy restrictions in such an environment. Queries and their associated data are cached at the mobile unit as a complete object. Consistency is maintained by using a parity-based invalidation scheme. Additionally, a simple prefetching scheme is used in conjunction with caching to further improve effectiveness of the proposed scheme.

Within the scope of the multithreaded dataflow, the problem of scheduling/allocation of DOACROSS loops has been discussed and it was shown that the so called staggered allocation offers higher performance and resource utilization than other schemes described in the literature. The staggered scheme, however, produces an unbalanced load among processors. The paper introduces an extension to the staggered scheme-cyclic staggered scheme-that produces a more balanced distribution of iterations among processors. The cyclic staggered scheme is simulated and its performance improvement is analyzed.

Cache memories have proven their effectiveness in the von Neumann architecture when localities of reference govern the execution loci of programs. A pure dataflow program, in contrast, contains no locality of reference since the execution sequence is enforced only by the availability of arguments. Instruction locality may be enhanced if, dataflow programs are reordered. Enhancing the locality of data references in the dataflow architecture is a more challenging problem. In this paper we report our approaches to the design of instruction, data (operand) and I-Structure cache memories using the Explicit Token Store (ETS) model of dataflow systems. We will present the performance results obtained using various benchmark programs.

A multidatabase system provides integrated access to heterogeneous, autonomous local databases in a distributed system. An important problem in current multidatabase systems is identification of semantically similar data in different local databases. The Summary Schemas Model (SSM) is proposed as an extension to multidatabase systems to aid in semantic identification. The SSM uses a global data structure to abstract the information available in a multidatabase system. This abstracted form allows users to use their own terms (imprecise queries) when accessing data rather than being forced to use system-specified terms. The system uses the global data structure to match the user's terms to the semantically closest available system terms. A simulation of the SSM is presented to compare imprecise-query processing with corresponding query-processing costs in a standard multidatabase system. The costs and benefits of the SSM are discussed, and future research directions are presented.

Efficient partitioning and scheduling of parallel programs and the distribution of data among pmessing elements are very important issues in parallel and distributed systems. Existing tools fdl short in addressing the issues satisfactorily. On one hand, it is believed to k unreasonable to leave the burden of these complex tasks to the programmers. On the other hand, fully automated schedulers have ken shown to be d little practical significance, or suitable only for restricted cases. In this paper we address the issues and algorithms for efficient partitioning and scheduling d parallel programs, including the distribution of data, in dislributed-memory multiprocessor systems, using the PARSA parallel software development environment. PARSA consists of a set of visual, interactive, compiletime tools that will provide automated program partitions and schedules whenever possible, while permitting the user to exert conml over these operations for a htter performance. The support program assessment tool provides the users the opportunity b fine-tune the program and achieve their performance objectives.

The asynchronous nature of the dataflow model of computation allows the exploitation of maximum inherent parallelism in many application programs. However, before the dataflow model of computation can become a viable alternative to the control flow model of computation, one has to find practical solutions to some problems such as efficient handling of data structures. The paper introduces a new model for handling data structures in a dataflow environment. The proposed model combines constant time access capabilities of vectors as well as the flexibility inherent in the concept of pointers. This allows a careful balance between copying and sharing to optimize the storage and processing overhead incurred during the operations on data structures. The mode) is compared by simulation to other data structure models proposed in the literature, and the results are good.>

The notion of CPRS (concurrent processing which result sharing) graph decomposition is presented which is based on problem dynamics, and the validity of the CPRS scheme in a dataflow environment is explored. A dynamic dataflow architecture to execute programs under the new model of computation, called the CPRS architecture, is presented. An analytical modeling, based on a closed queueing network model, is used to study the effect of result sharing on the performance of the CPRS architecture.>

Clusteringis an effective mechanism for retrieving complex objects.Many object-oriented database

article Free Access Share on A systolic multiplier unit and its VLSI design Authors: A. R. Hurson School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, Oklahoma School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, OklahomaView Profile , B. Shirazi School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, Oklahoma School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, OklahomaView Profile Authors Info & Claims ACM SIGARCH Computer Architecture NewsVolume 13Issue 3June 1985 pp 302–309https://doi.org/10.1145/327070.327274Published:01 June 1985Publication History 1citation428DownloadsMetricsTotal Citations1Total Downloads428Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF