Efficient Indexing Scheme Research Articles

An efficient color and texture based iris image retrieval technique

This paper proposes a hierarchical approach to retrieve an iris image efficiently from for a large iris database. This approach is a combination of both iris color and texture. Iris color is used for indexing and texture is used for retrieval of iris images from the indexed iris database. An index which is determined from the iris color is used to filter out the images that are not similar to the query image in color. Further, iris texture features of those filtered images, are used to determine the images which are similar to the query image. The iris color information helps to design an efficient indexing scheme based on color indices. The color indices are computed by averaging the intensity values of all red and blue color pixels. Kd-tree is used for real-time indexing based on color indices. The iris texture features are obtained through Speeded Up Robust Features (SURF) algorithm. These features are used to get the query’s corresponding image at the top best match. The performance of the proposed indexing scheme is compared with two well known iris indexing schemes ( Mehrotra, Majhi, & Gupta, 2010; Puhan & Sudha, 2008) on UPOL ( Dobeš, Machala, Tichavský, & Pospı´šil, 2004) and UBIRIS ( Proencca & Alexandre, 2005) iris databases. It is observed that combination of iris color and texture improves the performance of iris recognition system.

An Indexing and Query Processing Approach of RDF Data Based on Prüfer Sequence

As a web of data,the Semantic Web is assembling and organizing web information.Therefore,Semantic Web applications face the challenge of storage and processing of RDF data at a larger and larger scale.An efficient indexing scheme may be one of the solutions.The sequence-based indexing can bring good query performance with mature sequence matching techniques while keeping reasonable space consumption.In this paper,we extend the Prufer sequence approach to support index and query processing on RDF data,and implement a prototype system called Prig.Performance comparisons with Sesame RDF framework on LUBM benchmark and SP2Bench benchmark are presented.The experimental results illustrate that our approach has a better performance on large-scale RDF data.

M-MUSICS: an intelligent mobile music retrieval system

Accurate voice humming transcription and efficient indexing and retrieval schemes are essential to a large-scale humming-based audio retrieval system. Although much research has been done to develop such schemes, their performance in terms of precision, recall, and F-measure, among all similarity metrics, are still unsatisfactory. In this paper, we propose a new voice query transcription scheme. It considers the following features: note onset detection using dynamic threshold methods, fundamental frequency (F0) acquisition of each frame, and frequency realignment using K-means. We use a popularity-adaptive indexing structure called frequently accessed index (FAI) based on frequently queried tunes for indexing purposes. In addition, we propose a semi-supervised relevance feedback and query reformulation scheme based on a genetic algorithm to improve retrieval efficiency. In this paper, we extend our efforts to mobile multimedia environments and develop a mobile audio retrieval system. Experiments show our system performs satisfactory in wireless mobile multimedia environments.

속도 변화가 빈번한 이동 객체의 미래 시점 위치 추정에 적합한 색인 기법

정보 기술과 이동 통신망의 발전에 따라 이동 객체의 위치 추적에 필요한 효과적인 색인 기법과 이를 통한 다양한 응용 서비스에 대한 사용자 요구가 커지고 있다. 이에 따라, 이동 객체의 현재 위치 추적과 미래 위치 추정에 사용되는 <TEX>$TPR^*$</TEX>-트리와 같은 색인 기법이 관심을 받고 있다. <TEX>$TPR^*$</TEX>-트리는 정적인 물체를 색인하기 위해 고안된 R-트리를 기본 구조로 하기 때문에, 갱신 비용이 크다는 단점이 있다. 따라서 이동 객체가 자주 속도나 위치 정보를 갱신하는 경우 트리 유지비용이 빠르게 증가할 수 있다. 특히, 이동 객체가 빠른 속도로 이동하는 경우 넓은 범위에 걸쳐 불필요한 노드 갱신이 발생할 수 있다는 문제점이 있다. 이런 문제점을 피하기 위해 논문에서는 루트노드의 자식노드에 이런 빠른 이동 속도의 객체만을 따로 색인하도록 하여 노드 갱신 비용을 크게 줄이는 방법을 제안한다. 제안된 방법을 통해 노드 갱신 비용을 최소화 하면서도 <TEX>$TPR^*$</TEX>-트리의 장점을 유지할 수 있었다. 이런 성능상의 장점을 보이기 위해 시뮬레이션 기법을 사용한 성능 비교를 수행하였다. With the advances in the information technology and mobile communications, we now face increasing demands for various services based on both of position tracking of moving objects and their efficient index scheme. Accordingly, the <TEX>$TPR^*$</TEX>-tree, which were proposed for efficiently tracking moving objects and predicting their positions in the future time, has drawn much intention. As the <TEX>$TPR^*$</TEX>-tree came from the R-tree that is suitable for indexing static objects, it does not support cheap update costs. Therefore, it seems to be very costly to index moving objects if there are frequent occurrences of node updates caused by continuously changing velocities and positions. If some moving objects with high velocities have node updates, in particular, then the <TEX>$TPR^*$</TEX>-tree may suffer from many unnecessary updates in the wide range of tree regions. To avoid such a problem, we propose a method that can keep fast-moving objects in the child nodes of the root node, thereby saving node update costs in the <TEX>$TPR^*$</TEX>-tree. To show our performance advantages and retaining <TEX>$TPR^*$</TEX>-tree features, we performed some performance experiments using a simulation technique.

Path prediction and predictive range querying in road network databases

In automotive applications, movement-path prediction enables the delivery of predictive and relevant services to drivers, e.g., reporting traffic conditions and gas stations along the route ahead. Path prediction also enables better results of predictive range queries and reduces the location update frequency in vehicle tracking while preserving accuracy. Existing moving-object location prediction techniques in spatial-network settings largely target short-term prediction that does not extend beyond the next road junction. To go beyond short-term prediction, we formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical object trajectories. The model aims to capture the turning patterns at junctions and the travel speeds on road segments at the level of individual objects. Based on the mobility model, we present a maximum likelihood and a greedy algorithm for predicting the travel path of an object (for a time duration h into the future). We also present a novel and efficient server-side indexing scheme that supports predictive range queries on the mobility statistics of the objects. Empirical studies with real data suggest that our proposals are effective and efficient.

Finding Patterns In Given Intervals

In this paper, we study the pattern matching problem in given intervals. Depending on whether the intervals are given a priori for pre-processing, or during the query along with the pattern or, even in both the cases, we develop efficient solutions for different variants of this problem. In particular, we present efficient indexing schemes for each of the above variants of the problem.

Robust iris indexing scheme using geometric hashing of SIFT keypoints

This paper proposes an efficient indexing scheme for searching large iris biometric database that achieves invariance to similarity transformations, illumination and occlusion. The proposed scheme considers local descriptors as well as relative spatial configuration for claiming identity. To overcome the effect of non-uniform illumination and partial occlusion due to eyelids, local features are extracted from noise independent annular iris image using scale invariant feature transform (SIFT). The detected keypoints are used to index iris database by applying geometric hashing scheme that is robust to similarity transformations as well as occlusion. During iris retrieval, geometric hashed location from query iris image is obtained to access the appropriate bin of hash table and for every entry found there, a vote is casted. The iris images that receive more than certain number of votes are considered as possible candidates. In order to find the potential matches, the keypoint descriptor of the list of possible candidates is matched with the query iris. Since only small portion of database is scanned to find a match it reduces the query retrieval time and improves accuracy. This approach is tested on UBIRIS, BATH, CASIA and IITK iris databases and shows a substantial improvement over exhaustive search technique in terms of time and accuracy.

Embedded lattices tree: An efficient indexing scheme for content based retrieval on image databases

One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. To retrieve a particular image from a large scale image database, users can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the presence of a large amount of feature vectors in high-dimensional space. This paper addresses such problems and presents a novel indexing technique, the embedded lattices tree, which is designed to bring an effective solution especially for realizing the trade off between the retrieval speed up and precision. The embedded lattices tree is based on a lattice vector quantization algorithm that divides the feature vectors progressively into smaller partitions using a finer scaling factor. The efficiency of the similarity queries is significantly improved by using the hierarchy and the good algebraic and geometric properties of the lattice. Furthermore, the dimensionality reduction that we perform on the feature vectors, translating from an upper level to a lower one of the embedded tree, reduces the complexity of measuring similarity between feature vectors. In addition, it enhances the performance on nearest neighbor queries especially for high dimensions. Our experimental results show that the retrieval speed is significantly improved and the indexing structure shows no sign of degradations when the database size is increased.

A query index for continuous queries on RFID streaming data

RFID middleware collects and filters RFID streaming data to process applications’ requests called continuous queries, because they are executed continuously during tag movement. Several approaches to building an index on queries rather than data records, called a query index, have been proposed to evaluate continuous queries over streaming data. EPCglobal proposed an Event Cycle Specification (ECSpec) model, which is a de facto standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments that represent the query conditions. The problem when using any of the existing query indexes on these continuous queries is that it takes a long time to build the index, because it is necessary to insert a large number of segments into the index. To solve this problem, we propose a transform method that converts a group of segments into compressed data. We also propose an efficient query index scheme for the transformed space. Comparing with existing query indexes, the performance of proposed index outperforms the others on various datasets.

QUISIS: Interval Skip List를 활용한 질의 색인 기법

Due to the proliferation of the Internet and intranet, new application domains such as stream data processing have emerged. Stream data is real-timely and continuously generated. In stream data environments, a lot of queries are registered, and then, the arrived data item is evaluated by registered queries. Thus, to accelerate the query performance, diverse continuous query index schemes have been proposed for stream data processing systems. In this paper, we focus on the query index technique for stream data. In general, a stream query contains the range condition. Thus, by using range conditions, the queries can be indexed. In this paper, we propose an efficient query index scheme, called QUISIS, using a modified Interval Skip Lists to accelerate search time. QUISIS utilizes a locality where a value which will arrive in near future is similar to the current value. Through the experimental study, we show the efficiency of our proposed method.

Indexing schemes for similarity search in datasets of short protein fragments

We propose a family of very efficient hierarchical indexing schemes for ungapped, score matrix-based similarity search in large datasets of short (4–12 amino acid) protein fragments. This type of similarity search has importance in both providing a building block to more complex algorithms and for possible use in direct biological investigations where datasets are of the order of 60 million objects. Our scheme is based on the internal geometry of the amino acid alphabet and performs exceptionally well, for example outputting 100 nearest neighbours to any possible fragment of length 10 after scanning on average less than 1% of the entire dataset.

Hierarchical Cellular Tree: An Efficient Indexing Scheme for Content-Based Retrieval on Multimedia Databases

One of the challenges in the development of a content-based multimedia indexing and retrieval application is to achieve an efficient indexing scheme. The developers and users who are accustomed to making queries to retrieve a particular multimedia item from a large scale database can be frustrated by the long query times. Conventional indexing structures cannot usually cope with the requirements of a multimedia database, such as dynamic indexing or the presence of high-dimensional audiovisual features. Such structures do not scale well with the ever increasing size of multimedia databases whilst inducing corruption and resulting in an over-crowded indexing structure. This paper addresses such problems and presents a novel indexing technique, hierarchical cellular tree (HCT), which is designed to bring an effective solution especially for indexing large multimedia databases. Furthermore it provides an enhanced browsing capability, which enables user to make a guided tour within the database. A pre-emptive cell-search mechanism is introduced in order to prevent corruption, which may occur due to erroneous item insertions. Among the hierarchical levels that are built in a bottom-up fashion, similar items are collected into appropriate cellular structures at some level. Cells are subject to mitosis operations when the dissimilarity exceeds a required level. By mitosis operations, cells are kept focused and compact and yet, they can grow into any dimension as long as the compactness is maintained. The proposed indexing scheme is then used along with a recently introduced query method, the progressive query, in order to achieve the ultimate goal, from the user point of view that is retrieval of the most relevant items in the earliest possible time regardless of the database size. Experimental results show that the speed of retrievals is significantly improved and the indexing structure shows no sign of degradations when the database size is increased. Furthermore, HCT indexing body can conveniently be used for efficient browsing and navigation operations among the multimedia database items

Alternative graphical representation of determinantal many‐electron states

AbstractThe present approach is a graphical procedure for visualizing and addressing determinants and α and β strings for many‐electron systems. Compared with previous approaches, the emphasis in the graph is on the vertices and vertex weights. The arcs in the graph are only used for visualization. This leads to a compact representation of the configuration space and provides a computationally efficient and dense indexing scheme. The localization of inter configurational matrix elements are determined directly from the graph by means of segments and shapes similar to other graphical approaches. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2006

An Efficient Indexing Scheme for Range Aggregate Queries in Spatial Data Warehouse

An Efficient Indexing Scheme for Range Aggregate Queries in Spatial Data Warehouse

Efficient index caching for data dissemination in mobile computing environments

Due to the limited power supply of mobile devices, much research has been done on reducing the power consumption of mobile devices in mobile computing environments. Since supporting indices on the broadcast data items can effectively reduce the power consumption of the mobile clients, most of the existing research on data broadcasting has focused on designing efficient indexing schemes. In this paper, we propose to cache indices on the mobile clients and to use cached indices to facilitate the access of the broadcast data items. To manage the cache usage of the mobile clients, we propose two cache management policies. The lower-level-index-first policy caches the index nodes that are at the lower level of the index tree, while the cut-plane-first policy caches the index nodes that belong to a cut-plane of the index tree. Through experiments, we compare the performance of the two proposed policies with some existing policies in terms of tuning time and access time. The experiments show that index caching significantly reduces the tuning time of a mobile client without increasing its access time. In terms of tuning time, the experiments show that, when the access pattern of a mobile client is not skew, the cut-plane-first policy outperforms the lower-level-index first policy, LRU and LRFU. On the contrary, when the mobile client has a small cache and its access pattern is skew, the lower-level-index-first policy outperforms the cut-plane-first policy, LRU and LRFU. In terms of access time, the lower-level-index first policy outperforms the cut-plane-first policy, LRU and LRFU.

FMF: Query adaptive melody retrieval system

Recent progress of computer and network technologies makes it possible to store and retrieve a large volume of multimedia data in many applications. In such applications, efficient indexing scheme is very important for multimedia retrieval. Depending on the media type, multimedia data shows distinct characteristics and requires different approach to handle. In this paper, we propose a fast melody finder (FMF) that can retrieve melodies fast from audio database based on frequently queried tunes. Those tunes are collected from user queries and incrementally updated into index. Considering empirical user request pattern for multimedia data, those tunes will cover significant portion of user requests. FMF represents all the acoustic and common music notational inputs using a well-known string format such as UDR and LSR and uses string matching techniques to find query results. We implemented a prototype system and report on its performance through various experiments.

A Compressed Diagonals Remapping Technique for Dynamic Data Redistribution on Banded Sparse Matrix

In many scientific applications, dynamic data redistribution is used to enhance algorithm performance and achieve data locality in parallel programs on distributed memory multi-computers. In this paper, we present a new method, Compressed Diagonals Remapping (CDR) technique aims to the efficiency of runtime data redistribution on banded sparse matrices. The main idea of the proposed technique is first to compress the source matrix into a Compressed Diagonal Matrix (CDM) form. Based on the compressed diagonal matrix, a one-dimensional local and global index transformation method can be applied to carry out data redistribution on the compressed diagonal matrix. This process is identical to redistribute data in the two-dimensional banded sparse matrix. The CDR technique uses an efficient one-dimensional indexing scheme to perform data redistribution on banded sparse matrix. A significant improvement of this approach is that a processor does not need to determine the complicated sending or receiving data sets for dynamic data redistribution. The indexing cost is reduced significantly. The second advantage of the present techniques is the achievement of optimal packing/unpacking stages consequent upon the consecutive attribute of column elements in a compressed diagonal matrix. Another contribution of our methods is the ability to handle sparse matrix redistribution under two disjoint processor grids in the source and destination phases. A theoretical model to analyze the performance of the proposed technique is also presented in this paper. To evaluate the performance of our methods, we have implemented the present techniques on an IBM SP2 parallel machine along with the v2m algorithm and a dense redistribution strategy. The experimental results show that our technique provides significant improvement for runtime data redistribution of banded sparse matrices in most test samples.

Indexing Moving Points

We propose three indexing schemes for storing a set S of N points in the plane, each moving along a linear trajectory, so that any query of the following form can be answered quickly: Given a rectangle R and a real value t, report all K points of S that lie inside R at time t. We first present an indexing structure that, for any given constant ε>0, uses O( N/ B) disk blocks and answers a query in O(( N/ B) 1/2+ ε + K/ B) I/Os, where B is the block size. It can also report all the points of S that lie inside R during a given time interval. A point can be inserted or deleted, or the trajectory of a point can be changed, in O( log B 2 N) I/Os. Next, we present a general approach that improves the query time if the queries arrive in chronological order, by allowing the index to evolve over time. We obtain a tradeoff between the query time and the number of times the index needs to be updated as the points move. We also describe an indexing scheme in which the number of I/Os required to answer a query depends monotonically on the difference between the query time stamp t and the current time. Finally, we develop an efficient indexing scheme to answer approximate nearest-neighbor queries among moving points.

Querying Compressed Data in Data Warehouses

The large size of most data warehouses (typically hundreds of gigabytes to terabytes) results in non-trivial storage costs and makes compression techniques attractive. For the most part, page-level compression (as opposed to attribute or record level schemes) has been shown to achieve the greatest reductions in storage size for databases. A key issue with such schemes is how to quickly access the data to answer queries, since individual tuple boundaries are lost. In this paper we introduce an approach that aims to maintain the benefits of page-level compression (i.e., large reductions in storage size), while at the same time improving query performance through an efficient signature file indexing scheme. The approach uses an attribute-level signature generation method that exploits the value distribution of each attribute in a data warehouse. We provide an extensive theoretical analysis of this approach in which we compare our approach with a recently proposed indexing technique, encoded bitmapped indexing, along a number of important metrics including query processing, insertion, and storage costs. Results show that our approach is preferred in many situations that are likely to occur in practice. We have also implemented a prototype system which validates our analytical findings.

A graphical scheme for representing many‐electron spin‐free configurations

AbstractThe present approach is a graphical technique for representing and generating primitive configurations of space orbitals for electronic systems. The graph is developed as a tree whose paths define the allowed space configurations of electrons admitting at most double occupancy for each orbital. The emphasis in the graph is on the nodes representing occupied orbitals rather than on the arcs compared with previous graphical procedures used in graphical unitary and symmetric group approaches. This leads to a compact representation of the orbital configurations and provides a computationally very efficient indexing scheme. © 2001 John Wiley &amp; Sons, Inc. Int J Quantum Chem, 2001