Landmark-Join: Hash-Join Based String Similarity Joins with Edit Distance Constraints

Graphs are widely used to model complex data in many applications, such as bioinformatics, chemistry, social networks, pattern recognition. A fundamental and critical query primitive is to efficiently search similar structures in a large collection of graphs. This article mainly studies threshold-based graph similarity search with edit distance constraints. Existing solutions to the problem utilize fixed-size overlapping substructures to generate candidates, and thus become susceptible to large vertex degrees and distance thresholds. In this article, we present a partition-based approach to tackle the problem. By dividing data graphs into variable-size non-overlapping partitions, the edit distance constraint is converted to a graph containment constraint for candidate generation. We develop efficient query processing algorithms based on the novel paradigm. Moreover, candidate-pruning techniques and an improved graph edit distance verification algorithm are developed to boost the performance. In addition, a cost-aware graph partitioning method is devised to optimize the index. Extending the partition-based filtering paradigm, we present a solution to the top- $$k$$ graph similarity search problem, where tailored filtering, look-ahead and computation-sharing strategies are exploited. Using both public real-life and synthetic datasets, extensive experiments demonstrate that our approaches significantly outperform the baseline and its alternatives.

Approximate substring matching is a common problem in many applications. In this paper, we study approximate substring matching with edit distance constraints. Existing methods are very sensitive to query strings or query parameters like query length and edit distance. To address the problem, we propose a new approach using partition scheme. It first partitions a query into several segments, and finds matching substrings of these segments as candidates, then performs a bidirectional verification on these candidates to get final results. We devise an even partition scheme to efficiently find candidates, and a best partition scheme to find high quality candidates. Furthermore, through theoretical analysis, we find that the best partition scheme cannot always outperform the even partition scheme. Thus we propose an adaptive approach for selectively choosing scheme using statistic knowledge. We conduct comprehensive experiments to demonstrate the efficiency and quality of our proposed method.

Multifrequency Spin Wave Device for Parallel Data Processing using Micro Structured Yttrium Iron Garnet Thin Films

Cloud computing is a technology in which the Cloud Service Providers (CSP) provide many virtual servers to the users to store their information in the cloud. The faults occurring on the assignment and dismission of the virtual machines, the processing cost in the allocation of resources must also be considered. The parallel processing of the information on the virtual machines must be done effectively and in an efficient manner. A variety of systems were developed to facilitate Many Task Computing (MTC). These systems aim to hide the issues of parallelism and fault tolerant and they are used in many applications. In this paper, we introduced Nephele, a data processing framework to exploit dynamic resource provisioning offered by IaaS clouds. The performance evaluation of the virtual machines has been evaluated and the allocation and deallocation of job tasks to the specific virtual machines has also been considered. A performance comparison with the well known data processing framework hadoop has been done. Thus this paper tells about the effective and efficient manner of processing the data by parallel processing and allocating the correct resources for the desired task. It also helps to reduce the cost of resource utilization by exploiting the dynamic resource utilization.

Dictionary-based entity extraction has attracted much attention from the database community recently, which locates sub strings in a document into predefined entities (e.g., person names or locations). To improve extraction recall, a recent trend is to provide approximate matching between sub strings of the document and entities by tolerating minor errors. In this paper we study dictionary-based approximate entity extraction with edit-distance constraints. Existing methods have several limitations. First, they need to tune many parameters to achieve high performance. Second, they are inefficient for large edit-distance thresholds. We propose a trie-based method to address these problems. We first partition each entity into a set of segments, and then use a trie structure to index segments. To extract similar entities, we search segments from the document, and extend the matching segments in both entities and the document to find similar pairs. We develop an extension-based method to efficiently find similar string pairs by extending the matching segments. We optimize our partition scheme and select the best partition strategy to improve the extraction performance. Experimental results show that our method achieves much higher performance compared with state-of-the-art studies.

Neuroalgorithms and neuronetwork structures were analyzed, basic components of neuronetworks were defined and the principles of their development were chosen. It was shown that using the method of parallel vertical group data processing for the implementation of the neuronetworks basic components provides speed increase, reduce of hardware costs and increasing of the equipment use efficiency. Parallel vertical group codes converter, which provides time alignment of data receipt processes and bit sections formation, was developed. The methods and the structures of the components with parallel vertical group data processing for definition of maximum and minimum numbers in the arrays, calculation of the sum of differences squares and scalar product, which due to parallel processing of bit sections groups, provide speed increase, were developed. It was shown that use of the developed basic components for neuronetworks synthesis will provide reduction of time and development cost.

We apply optical parallel processing to operations for multiplication modulo, which is one of the key components of a factorization algorithm. With this method, optical phase modulation provides the results of modulo operations. We construct a prototype system based on a Michelson interferometer with a photodetector array. Mirrors are set at both object and reference arms to generate interference fringes. A mirror in the object arm is tilted slightly, whereas the reference arm is set perpendicular to the optical axis. The tilt angle is determined by parameters for the target modulo operations. The presented system can achieve massive data processing in parallel with only simple implementation. We present our experimental results to verify the usefulness of our method.

With the rapid development of network technology, especially in the internet area, distributed computation and parallel processing of data are urgently needed. This paper studies the combination operation of the lattice-valued concept lattices to realize its distributed computation and parallel processing. The proposal of its corresponding combination algorithm is mainly according to the conjunction properties of incomparable attribute values. Firstly, we define a homotypic lattice-valued concept lattice and a combined concept lattice; secondly, we analyze some relations between the lattice-valued formal concepts under two conditions and prove the isomorphism of the combination theory; finally, we present the combination algorithm of multiple lattice-valued concept lattices and analyze the algorithm complexity and employ an example to show the application of this combination algorithm.

BackgroundTerm clustering, by measuring the string similarities between terms, is known within the natural language processing community to be an effective method for improving the quality of texts and dictionaries. However, we have observed that chemical names are difficult to cluster using string similarity measures. In order to clearly demonstrate this difficulty, we compared the string similarities determined using the edit distance, the Monge-Elkan score, SoftTFIDF, and the bigram Dice coefficient for chemical names with those for non-chemical names.ResultsOur experimental results revealed the following: (1) The edit distance had the best performance in the matching of full forms, whereas Cohen et al. reported that SoftTFIDF with the Jaro-Winkler distance would yield the best measure for matching pairs of terms for their experiments. (2) For each of the string similarity measures above, the best threshold for term matching differs for chemical names and for non-chemical names; the difference is especially large for the edit distance. (3) Although the matching results obtained for chemical names using the edit distance, Monge-Elkan scores, or the bigram Dice coefficients are better than the result obtained for non-chemical names, the results were contrary when using SoftTFIDF. (4) A suitable weight for chemical names varies substantially from one for non-chemical names. In particular, a weight vector that has been optimized for non-chemical names is not suitable for chemical names. (5) The matching results using the edit distances improve further by dividing a set of full forms into two subsets, according to whether a full form is a chemical name or not. These results show that our hypothesis is acceptable, and that we can significantly improve the performance of abbreviation-full form clustering by computing chemical names and non-chemical names separately.ConclusionsIn conclusion, the discriminative application of string similarity methods to chemical and non-chemical names may be a simple yet effective way to improve the performance of term clustering.

Cyber-physical applications need to process a lot of sensor data, for example, to analyze traffic patterns and structural soundness of critical infrastructures. Although the amount of sensor data to process is increasing fast, system support to efficiently store and analyze an extensive amount of sensor data largely lags behind. To efficiently store, retrieve, and process massive sensor data, we are developing a sensor data center (SDC) that supports spatio-temporal sensor data structures and parallel sensor data processing using clustered computational nodes composed of commodity hardware. The SDC sharply contrasts to most existing data centers that do not support spatio-temporal sensor data storage, retrieval, and processing. In this paper, we especially focus on the problem of potential load imbalance due to data access skews that adversely affects the timeliness of parallel sensor data processing. Specifically, we present an adaptive data replication method to address access skews in a SDC. In our performance evaluation performed in a preliminary version of a SDC, our adaptive approach substantially outperforms a baseline that does not support adaptive data replication.

The authors report online parallel data processing in the GA.SP (gamma spectrometer) data acquisition system. A transputer network (TNET) has been built as a dedicated parallel machine for high-speed data processing and a nuclear experiment oriented parallel language (NEO) has been designed to support parallel programming on TNET. Event pipeline analyzing domain data structures and interactive communication with the program are characteristics of the NEO language. A multichain pipeline machine and its shell are also presented to show how to run the NEO program and to operate the TNET efficiently.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Large-scale visualization systems are typically designed to efficiently “push” datasets through the graphics hardware. However, exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a first step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Specifically, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and mesh simplification) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simplification of meshes with 30GBs of data and subsequent rendering with image resolutions up to 80000 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization system deployed in a public cloud or a local commodity cluster.

The paper presents a modification of the structure of a biological neural network (BNN) based on spiking neuron models. The proposed modification allows to influence the level of the stimulus response of particular neurons in the BNN. We consider an extended, three-dimensional Hodgkin-Huxley model of the neural cell. A typical BNN composed of such neural cells have been expanded by addition of resistors in each branch point. The resistors can be treated as the weights in such BNN. We demonstrate that adding these elements to the BNN significantly affects the waveform of the potential on the membrane of the neuron, causing an uncontrolled excitation. This provides a better description of processes that take place in nervous cell. Such BNN enables an easy adaptation of the learning rules used in artificial or spiking neural networks. The modified BNN has been implemented on Graphics Processing Unit (GPU) in the CUDA C language. This platform enables a parallel data processing, which is an important feature in such applications.

Algorithmically optimized AVC (MPEG-4 part 10/H.264) video encoder with parallel processing of data is presented in the paper. The paper reveals the architecture of the proposed encoder together with the description of the applied software optimization techniques. Conducted experiments show exactly the degree of parallelization of computations in the encoder and the compression performance of video encoding.

The article describes a new approach to the processing of long series of ozone monitoring data in the surface atmosphere called the Data Parallel Processing in Block Streams. The proposed method is based on splitting of a sequential series of initial data into blocks filled with surface ozone monitoring data for one day. Further, a chain is formed from such blocks, the length of which is determined by the total length of the monitoring process. Along this chain of blocks, parallel processing of the initial data is carried out, aimed at smoothing out fast fluctuations. The smoothed data is then used to determine the daily production of ozone due to photochemical reactions, its minimum nighttime levels, as well as the magnitude of nighttime maxima. The possibilities of the proposed approach are demonstrated using the analysis of surface ozone monitoring data in Moscow, Russia, in 2020. The prospects for further application of the developed method are discussed.