6072229
PRESTO: feedback-driven data management in sensor networks
This paper presents PRESTO, a novel two-tier sensor data management architecture comprising proxies and sensors that cooperate with one another for acquiring data and processing queries. PRESTO proxies construct time-series models of observed trends in the sensor data and transmit the parameters of the model to sensors. Sensors check sensed data with model-predicted values and transmit only deviations from the predictions back to the proxy. Such a model-driven push approach is energy-efficient, while ensuring that anomalous data trends are never missed. In addition to supporting queries on current data, PRESTO also supports queries on historical data using interpolation and local archival at sensors. PRESTO can adapt model and system parameters to data and query dynamics to further extract energy savings. We have implemented PRESTO on a sensor testbed comprising Intel Stargates and Telos Motes. Our experiments show that in a temperature monitoring application, PRESTO yields one to two orders of magnitude reduction in energy requirements over on-demand, proactive or model-driven pull approaches. PRESTO also results in an order of magnitude reduction in query latency in a 1% duty-cycled five hop sensor network over a system that forwards all queries to remote sensor nodes.
16584711
PeerWindow: an efficient, heterogeneous, and autonomic node collection protocol
Nodes in peer-to-peer systems need to know the information about others to optimize neighbor selection, resource exchanging, replica placement, load balancing, query optimization, and other collaborative operations. However, how to collect this information effectively is still an open issue. In this paper, we propose a novel information collection protocol, PeerWindow, with which each node can collect a large amount of pointers to other nodes at a very low cost. Compared to existing protocols, PeerWindow is 1) efficient, the cost of collecting 1,000 pointers being less than 1 kbps in a common system environment, 2) heterogeneous, nodes with different capacities collecting different amounts of information, and 3) autonomic, nodes determining their bandwidth cost for node collection by themselves and adjusting it dynamically. PeerWindow can be used in many existing peer-to-peer systems and has tremendous potential for future expansions.
1558162
Monitoring Abnormal Patterns with Complex Semantics over ICU Data Streams
Monitoring abnormal patterns in data streams is an important research area for many applications. In this paper we present a new approach MAPS(Monitoring Abnormal Patterns over data Streams) to model and identify the abnormal patterns over the massive data streams. Compared with other data streams, ICU streaming data have their own features: pseudo-periodicity and polymorphism. MAPS first extracts patterns from the online arriving data streams and then normalizes them according to their pseudo-periodic semantics. Abnormal patterns will be detected if they are satisfied the predicates defined in the clinician-specifying normal patterns. At last, a real application demonstrates that MAPS is efficient and effective in several important aspects.
5393650
PaDDMAS: parallel and distributed data mining application suite
Discovering complex associations, anomalies and patterns in distributed data sets is gaining popularity in a range of scientific, medical and business applications. Various algorithms are employed to perform data analysis within a domain, and range from statistical to machine learning and AI based techniques. Several issues need to be addressed however to scale such approaches to large data sets, particularly when these are applied to data distributed at various sites. As new analysis techniques are identified, the core tool set must enable easy integration of such analytical components. Similarly, results from an analysis engines must be sharable, to enable storage, visualisation or further analysis of results. We describe the architecture of PaDDMAS, a component based system for developing distributed data mining applications. PaDDMAS provides a tool set for combining pre-developed or custom components using a dataflow approach, with components performing analysis, data extraction or data management and translation. Each component is wrapped as a Java/CORBA object, and has an interface defined in XML. Components can be serial or parallel objects, and may be binary or contain a more complex internal structure. We demonstrate a prototype using a neural network analysis algorithm.
15392929
Optimality of Wait-Free Atomic Multiwriter Variables
Abstract Known implementations of concurrent wait-free atomic shared multiwriter variables use Θ( n ) control bits per subvariable. It has been shown that implementations of sequential time-stamp systems require Ω( n ) control bits per subvariable. We exhibit a sequential atomic shared multiwriter variable construction using log n control bits per subvariable. There arises the question of the optimality of concurrent implementations of the same, and of weak time-stamp systems. We also show that our solutions are self-stabilizing.
14317372
Construct piecewise Hermite interpolation surface with blending methods
Three methods are proposed to construct a piecewise Hermite interpolation surface (PHIS), which is a piecewise algebraic surface interpolating a set of given points with associated normal directions. The surface is obtained by blending together some low-degree surface patches. Both the first and the second methods are completely local and give a surface with G/sup n/-continuity. In the third construction, we reduce the number of surface patches by joining as many cubic patches as possible. This method gives a global solution with G/sup 1/-continuity. These three different methods can be used to meet different requirements of the designers.
27873507
Average-Case Analysis via Incompressibility
We will demonstrate how to use Kolmogorov complexity to do the average-case analysis via some examples. These examples include: longest common subsequence problem and shortest common supersequence problem [9, 11], problems in computational geometry [14], average case analysis of Heapsort [19, 17], average nni-distance between two binary rooted leave-labeled trees [23], compact routing in computer networks.