Answering aggregate queries like sum, count, min, max over regions containing moving objects is often needed for virtual world applications, real-time monitoring systems, etc. Since the data set is usually very large and some queries require significant processing resources, quite often such data is stored in a distributed system wherein each system handles a partition of the whole space and ma...

Structural health monitoring (SHM) involves continuous monitoring of a structure’s condition with near real-time analysis of sensed data. The emerging wireless sensor network (WSN) technology enables distributed data acquisition and processing for structure monitoring applications, which currently adopt a centralized solution, using a network of intelligent sensors. We design a robust WSN-based...

LH*g is a high-availability variant of the LH* Scalable Distributed Data Structure. An LH*g file scales up with constant key search and insert performance, while surviving any single-site unavailability (failure). Highavailability is achieved through record grouping. A group is a logical structure of up to k records, where k is a file parameter. The group members always remain at different site...

Different structures are used in peer-to-peer networks to represent their inherently distributed, self-organized, and decentralized memory structure. In this paper, a simple range-queriable distributed data structure, called RAQ, is proposed to efficiently support exact match and range queries over multi-dimensional data. In RAQ, the key space is partitioned among the network with n nodes, in w...

In this paper, we describe the I-Structure Software Cache for distributed memory environments (D-ISSC), which takes advantage of data locality while maintaining the capability of latency tolerance of I-Structure memory systems. D-ISSC facilitates the programming of MPI programs hiding synchronization issues from the programmer. Our experimental evaluation using a set of benchmarks indicates tha...

Applications with dynamic data structures, unpredictable computational costs, and irregular data access patterns require substantial eeort to parallelize. Much of their programming complexity comes from the implementation of distributed data structures. We describe a library of such data structures, Multipol, which includes parallel versions of classic data structures such as trees, sets, lists...

The Joint Center for Structural Genomics (JCSG), one of four large-scale structure-determination centers funded by the US Protein Structure Initiative (PSI) through the National Institute for General Medical Sciences, has been operating an automated distributed structure-solution pipeline, Xsolve, for well over half a decade. During PSI-2, Xsolve solved, traced and partially refined 90% of the ...

We propose a collective method to address the problem of learning the structure of a Bayesian network from a distributed heterogeneous data sources. In this case, the dataset is distributed among several sites, with different features at each site. The collective method has four steps: local learning, sample selection, cross learning, and combination of the results. The parents of local nodes c...

Distributed constraint optimization imposes considerable complexity in agents’ coordinated search for an optimal solution. However, in many application domains, problems often exhibit special structures that can be exploited to facilitate more efficient problem solving. One of the most recurrent structures involves disparity among subpmblems. We present a coordination mechanism, Anchor&Ascend, ...

The idea of a “memetic” spread of solutions through a human culture in parallel to their development is applied as a distributed approach to learning. Local parts of a problem are associated with a set of overlapping localities in a space and solutions are then evolved in those localities. Good solutions are not only crossed with others to search for better solutions but also they propagate acr...