Papers by Keyword: Indexing

Abstract: The suffix tree is a useful data structure constructed for indexing strings. However, when it comes to large datasets of discrete contents, most existing algorithms become very inefficient. Discrete datasets are need to be indexed in many fields like record analysis, data analyze in sensor network, association analysis etc. This paper presents an algorithm, STD, which stands for Suffix Tree for Discrete contents, that performs very efficiently with discrete input datasets. It imports several wonderful intermediate data structures for discrete strings; we also take care of the situation that the discrete input strings have similar characteristics. Moreover, STD keeps the advantages of existing implementations which are for successive input strings. Experiments were taken to evaluate the performance and shown that the method works well.

Abstract: Image data set are usually very large, which might consist of millions of image objects, it is essential to use an efficient and effective indexing technique to facilitate speedy searching. The features can be expressed in terms of high-dimensional vector data which can be compared with a given query for similarity between them. It is more important that the image database should be preprocessed and establish indexing to improve retrieval efficiency. In this paper, the method of improved X-tree is proposed, design and implementation of a high dimensional index application to facilitate the speedy searching in feature based image information retrieval. Compared by retrieval efficiency and retrieval result, it is convincingly proved that hierarchical index structure based on clustering is efficient and applicable in image characteristics indexing.

Abstract: Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive data sets. Big data service is very attractive in the field of wireless communication environment, especially when we face the spatial applications, which are typical applications of big data. Because of the complexity to ingest, store and analyze geographical information data, this paper reflects on a few of the technical problems presented by the exploration of big data, and puts forward an effective storage method in wireless communication environment, which is based on the measurement of moving regularity through proposing three key techniques: partition technique, index technique and prefetch technique. Experimental results show that the performance of big data storage method using these new techniques is better than the other storage methods on managing a great capacity of big data in wireless communication environment.

Abstract: EXPO2009 [1], the updated version of EXPO2004 [2], performs all the steps for the ab-initio crystal structure solution by powder diffraction data in a completely automatic way: unit cell determination, space group identification, decomposition of the experimental pattern into single integrated intensities, structure solution by traditional (Direct/Patterson) or direct space/hybrid approaches, structure refinement by Rietveld techniques [3]. New procedures have been introduced in EXPO2009 to strengthen its phasing ability, especially for organic compounds and for low resolution data, for which traditional approaches may fail.

Abstract: While various software packages exist to study powder patterns, few are accessible to beginners and yet remain highly customisable. In this paper we will give guidelines for biologists interested in analysing powder patterns of proteins with Topas. Several topics will be discussed, from basic methods like indexing on a restricted list of spacegroups, to advanced use of command input les for pattern modelling and rigid body renement.