Efficient Data Retrieval Research Articles

Securing sensor data storage and query based on k‐out‐of‐ n coding

SUMMARYWith in‐network sensor data storage and query, sensed data are stored locally and further accessed by users on demand. Thus, without proper protection for sensor data and user queries, compromise of sensor nodes may reveal sensitive information about the sensed environment as well as users’ query patterns. Furthermore, the adversary can alter some stored data and prevent users from recovering information correctly. In this paper, we propose and analyze the use of maximum‐distance separable codes to address the problem. First, initial data is encoded in (n, k) maximum‐distance separable code and spread from a single data source to a set of storage nodes for higher survivability. Second, a blind signature‐based privacy scheme is explored to preserve user query, such that authorized user's identity and privacy interests are concealed from others. Finally, an efficient data retrieval scheme is carried out such that the redundancy symbols in the remaining nodes are accessed only if the user fails to decode the data, which reduces the communication and computation cost. Analysis and evaluation verify the efficiency of the proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.

A case study for efficient management of high throughput primary lab data

BackgroundIn modern life science research it is very important to have an efficient management of high throughput primary lab data. To realise such an efficient management, four main aspects have to be handled: (I) long term storage, (II) security, (III) upload and (IV) retrieval.FindingsIn this paper we define central requirements for a primary lab data management and discuss aspects of best practices to realise these requirements. As a proof of concept, we introduce a pipeline that has been implemented in order to manage primary lab data at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). It comprises: (I) a data storage implementation including a Hierarchical Storage Management system, a relational Oracle Database Management System and a BFiler package to store primary lab data and their meta information, (II) the Virtual Private Database (VPD) implementation for the realisation of data security and the LIMS Light application to (III) upload and (IV) retrieve stored primary lab data.ConclusionsWith the LIMS Light system we have developed a primary data management system which provides an efficient storage system with a Hierarchical Storage Management System and an Oracle relational database. With our VPD Access Control Method we can guarantee the security of the stored primary data. Furthermore the system provides high performance upload and download and efficient retrieval of data.

Motion Retrieval Using Low‐Rank Subspace Decomposition of Motion Volume

AbstractThis paper proposes a novel framework that allows for a flexible and an efficient retrieval of motion capture data in huge databases. The method first converts an action sequence into a novel representation, i.e. the Self‐Similarity Matrix (SSM), which is based on the notion of self‐similarity. This conversion of the motion sequences into compact and low‐rank subspace representations greatly reduces the spatiotemporal dimensionality of the sequences. The SSMs are then used to construct order‐3 tensors, and we propose a low‐rank decomposition scheme that allows for converting the motion sequence volumes into compact lower dimensional representations, without losing the nonlinear dynamics of the motion manifold. Thus, unlike existing linear dimensionality reduction methods that distort the motion manifold and lose very critical and discriminative components, the proposed method performs well even when inter‐class differences are small or intra‐class differences are large. In addition, the method allows for an efficient retrieval and does not require the time‐alignment of the motion sequences. We evaluate the performance of our retrieval framework on the CMU mocap dataset under two experimental settings, both demonstrating promising retrieval rates.

원격 저장소 환경에서 다중 키워드를 이용한 효율적인 검색 가능한 대칭키 암호 시스템

매우 가벼운 무게와 손안에 들어가는 작은 크기로 휴대성을 제공하는 휴대용 저장매체는 사용자들로부터 많은 호응을 받고 있다. 하지만 휴대용 저장매체의 휴대성으로 인하여 USB 메모리의 분실 및 도난이 잦아졌고 그로 인하여 저장매체 내부에 저장되어 있는 개인정보가 유출되는 사고가 발생하는 문제점이 발생되었다. 따라서 분실위험이 없고 네트워크를 통하여 언제든 자료를 저장하고 접근할 수 있는 원격 저장소 서비스가 등장하게 되었다. 정보통신산업이 발달함에 따라 여러 종류의 정보 기기를 통하여 언제 어디서든 빠른 네트워크에 접근할 수 있게 되었고, 이는 원격 저장소를 이용하는 사용자들을 더욱 증가하게 하였다. 하지만 여러 사용자의 주요 자료가 저장됨에 따라 비윤리적인 관리자 및 공격자로 인하여 서버에 저장된 여러 사용자의 주요자료가 동시에 유출될 수 있는 위험이 존재한다. 이를 해결하기 위해 서버에 저장되는 자료의 암호화가 필요해졌으며, 이와 동시에 암호화한 자료의 효율적인 검색 및 이용을 위하여 검색 가능 암호 시스템이 필요하다. 하지만, 기존의 대칭키 검색 가능 암호 시스템은 문서의 삽입/삭제 효율성 및 다중 키워드 검색 시 연산의 효율성이 떨어지는 문제점이 존재한다. 따라서 본 논문은 기존 대칭키 검색 가능 암호의 문제점을 해결할 수 있는 효율적인 대칭키 검색 가능 암호 시스템을 제안한다. Removable Storage provides the excellent portability with light weight and small size which fits in one's hand, many users have recently turned attention to the high-capacity products. However, due to the easy of portability for Removable Storage, Removable Storage are frequently lost and stolen and then many problems have been occurred such as the leaking of private information to the public. The advent of remote storage services where data is stored throughout the network, has allowed an increasing number of users to access data. The main data of many users is stored together on remote storage, but this has the problem of disclosure by an unethical administrator or attacker. To solve this problem, the encryption of data stored on the server has become necessary, and a searchable encryption system is needed for efficient retrieval of encrypted data. However, the existing searchable encryption system has the problem of low efficiency of document insert/delete operations and multi-keyword search. In this paper, an efficient searchable encryption system is proposed.

Dealing with Nonuniformity in Data Centric Storage for Wireless Sensor Networks

In-network storage of data in Wireless Sensor Networks (WSNs) is considered a promising alternative to external storage since it contributes to reduce the communication overhead inside the network. Recent approaches to data storage rely on Geographic Hash Tables (GHT) for efficient data storage and retrieval. These approaches, however, assume that sensors are uniformly distributed in the sensor field, which is seldom true in real applications. Also they do not allow tuning the redundancy level in the storage according to the importance of the data to be stored. To deal with these issues, we propose an approach based on two mechanisms. The first is aimed at estimating the real network distribution. The second exploits data dispersal method based on the estimated network distribution. Experiments through simulation show that our approach approximates quite closely the real distribution of sensors and that our dispersal protocol sensibly reduces data losses due to unbalanced data load.

“Padding” bitmaps to support similarity and mining

The current paper presents a novel approach to bitmap-indexing for data mining purposes. Currently bitmap-indexing enables efficient data storage and retrieval, but is limited in terms of similarity measurement, and hence as regards classification, clustering and data mining. Bitmap-indexes mainly fit nominal discrete attributes and thus unattractive for widespread use, which requires the ability to handle continuous data in a raw format. The current research describes a scheme for representing ordinal and continuous data by applying the concept of "padding" where each discrete nominal data value is transformed into a range of nominal-discrete values. This "padding" is done by adding adjacent bits "around" the original value (bin). The padding factor, i.e., the number of adjacent bits added, is calculated from the first and second derivative degrees of each attribute's domain-distribution. The padded representation better supports similarity measures, and therefore improves the accuracy of clustering and mining. The advantages of padding bitmaps are demonstrated on Fisher's Iris dataset.

PheMaDB: A solution for storage, retrieval, and analysis of high throughput phenotype data

BackgroundOmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth conditions such as different metabolites and antibiotics over a time course of hours to days. In order to manage the large amount of data produced from the OmniLog™ instrument, PheMaDB (Phenotype Microarray DataBase), a web-based relational database, was designed. PheMaDB enables efficient storage, retrieval and rapid analysis of the OmniLog™ PM data.DescriptionPheMaDB allows the user to quickly identify records of interest for data analysis by filtering with a hierarchical ordering of Project, Strain, Phenotype, Replicate, and Temperature. PheMaDB then provides various statistical analysis options to identify specific growth pattern characteristics of the experimental strains, such as: outlier analysis, negative controls analysis (signal/background calibration), bar plots, pearson's correlation matrix, growth curve profile search, k-means clustering, and a heat map plot. This web-based database management system allows for both easy data sharing among multiple users and robust tools to phenotype organisms of interest.ConclusionsPheMaDB is an open source system standardized for OmniLog™ PM data. PheMaDB could facilitate the banking and sharing of phenotype data. The source code is available for download at http://phemadb.sourceforge.net.

Developing an Institutional Informational Base and Biblio-graphical Clearinghouse

Successful management and implementation of the diverse functions of the International Network of Person-Centered Medicine (INPCM) require a comprehensive and efficient informational base to advance quality of patient care though timely and rapid distribution of knowledge via publications, conferences, and education programs in concert with catalyzing research through systematic efficient data acquisition, storage, retrieval, and analysis. This study describes the structure and functions of the proposed INPCM's information system.

Content based Image Retrieval for Medical Images using Canny Edge Detection Algorithm

The rapid expansion of digital data content has led to the need for rich descriptions and efficient Retrieval Tool. To develop this, content based image Retrieval method has played an important role in the field of image retrieval. This paper aims to provide an efficient medical image data Retrieval from a huge content of medical database using one of the images content such as image shape, because, efficient content-based image Retrieval in the medical domain is still a challenging problem. The main objective of this paper is to provide an efficient tool which is used for efficient medical image retrieval from a huge content of medical image database and which is used for further medical diagnosis purposes.

A unified multimedia and semantic perspective for data retrieval in the semantic web

In recent years, the emerging diffusion of peer-to-peer networks is going beyond the single-domain paradigm like, for instance, the mono-thematic file sharing one (e.g. Napster for music). Peers are more and more heterogeneous data sources which need to share data with commercial, educational, and/or collaboration purposes, just to mention a few. Moreover, in current information processing applications data cannot be meaningfully searched by precise database queries that would return exact matches (e.g. when dealing with multimedia, proteomic, statistical data). In this paper we move a step towards multi-domain multi-type data sharing systems by introducing an advanced technological infrastructure which enables users to meet these new emerging needs. A fundamental issue in this context is data heterogeneity, which is pervasive and intrinsically present both at intensional level where, due to peers’ autonomy, different semantic descriptions of the available information are provided, and at extensional level, where multiple data types can coexist, also including content-based searchable data types such as multimedia data. Our proposal relies on a Peer Data Management Systems (PDMS) framework to present innovative network organization and query routing mechanisms which exploit both peers’ data description and data content to achieve effective and efficient network management and data retrieval in such a context. The validity of our proposal is demonstrated by an absolutely satisfactory experimental evaluation on a real setting.

BeSS, the official Be Star Spectra database

AbstractWe present the status of the BeSS database, which contains a catalogue of all known classical Be stars and a large collection of their spectra obtained at any wavelength, any epoch, and from various sources, from amateur astronomer spectra to professional high-resolution high signal-to-noise echelle spectra. Efficient data retrieval in such a heterogeneous data collection is possible with a wide range of selection criteria thanks to their storage in the fits format and via a web interface (http://basebe.obspm.fr) as well as via the Virtual Observatory. BeSS already contains over 49000 spectra and has allowed the detection of several outbursts.

A Reinforcement Learning Based Framework for Prediction of Near Likely Nodes in Data-Centric Mobile Wireless Networks

Data-centric storage provides energy-efficient data dissemination and organization for the increasing amount of wireless data. One of the approaches in data-centric storage is that the nodes that collected data will transfer their data to other neighboring nodes that store the similar type of data. However, when the nodes are mobile, type-based data distribution alone cannot provide robust data storage and retrieval, since the nodes that store similar types may move far away and cannot be easily reachable in the future. In order to minimize the communication overhead and achieve efficient data retrieval in mobile environments, we propose a reinforcement learning-based framework called PARIS, which utilizes past node trajectory information to predict the near likely nodes in the future as the best content distributee. Our framework can adaptively improve the prediction accuracy by using the reinforcement learning technique. Our experiments demonstrate that our approach can effectively and efficiently predict the future neighborhood.

Strategies for the Curation of CAD Engineering Models

Product Lifecycle Management (PLM) has become increasingly important in the engineering community over the last decade or so, due to the globalisation of markets and the rising popularity of products provided as services. It demands the efficient capture, representation, organisation, retrieval and reuse of product data over its entire life. Simultaneously, there is now a much greater reliance on CAD models for communicating designs to manufacturers, builders, maintenance crews and regulators, and for definitively expressing designs. Creating the engineering record digitally, however, presents problems not only for its long-term maintenance and accessibility - due in part to the rapid obsolescence of the hardware, software and file formats involved - but also for recording the evolution of designs, artefacts and products. We examine the curation and preservation requirements in PLM and suggest ways of alleviating the problems of sustaining CAD engineering models through the use of lightweight formats, layered annotation and the collection of Representation Information as defined in the Open Archival Information System (OAIS) Reference Model. We describe two tools which have been specifically developed to aid in the curation of CAD engineering models in the context of PLM: Lightweight Models with Multilayered Annotation (LiMMA) and a Registry/Repository of Representation Information for Engineering (RRoRIfE).

Discerning Tumor Status from Unstructured MRI Reports—Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing

Information in electronic medical records is often in an unstructured free-text format. This format presents challenges for expedient data retrieval and may fail to convey important findings. Natural language processing (NLP) is an emerging technique for rapid and efficient clinical data retrieval. While proven in disease detection, the utility of NLP in discerning disease progression from free-text reports is untested. We aimed to (1) assess whether unstructured radiology reports contained sufficient information for tumor status classification; (2) develop an NLP-based data extraction tool to determine tumor status from unstructured reports; and (3) compare NLP and human tumor status classification outcomes. Consecutive follow-up brain tumor magnetic resonance imaging reports (2000–­2007) from a tertiary center were manually annotated using consensus guidelines on tumor status. Reports were randomized to NLP training (70%) or testing (30%) groups. The NLP tool utilized a support vector machines model with statistical and rule-based outcomes. Most reports had sufficient information for tumor status classification, although 0.8% did not describe status despite reference to prior examinations. Tumor size was unreported in 68.7% of documents, while 50.3% lacked data on change magnitude when there was detectable progression or regression. Using retrospective human classification as the gold standard, NLP achieved 80.6% sensitivity and 91.6% specificity for tumor status determination (mean positive predictive value, 82.4%; negative predictive value, 92.0%). In conclusion, most reports contained sufficient information for tumor status determination, though variable features were used to describe status. NLP demonstrated good accuracy for tumor status classification and may have novel application for automated disease status classification from electronic databases.

Efficient motion data indexing and retrieval with local similarity measure of motion strings

Widely used in data-driven computer animation, motion capture data exhibits its complexity both spatially and temporally. The indexing and retrieval of motion data is a hard task that is not totally solved. In this paper, we present an efficient motion data indexing and retrieval method based on self-organizing map and Smith–Waterman string similarity metric. Existing motion clips are first used to train a self-organizing map and then indexed by the nodes of the map to get the motion strings. The Smith–Waterman algorithm, a local similarity measure method for string comparison, is used in clustering the motion strings. Then the motion motif of each cluster is extracted for the retrieval of example-based query. As an unsupervised learning approach, our method can cluster motion clips automatically without needing to know their motion types. Experiment results on a dataset of various kinds of motion show that the proposed method not only clusters the motion data accurately but also retrieves appropriate motion data efficiently.

Design of Multimodal Dissimilarity Spaces for Retrieval of Video Documents

This paper proposes a novel representation space for multimodal information, enabling fast and efficient retrieval of video data. We suggest describing the documents not directly by selected multimodal features (audio, visual or text), but rather by considering cross-document similarities relatively to their multimodal characteristics. This idea leads us to propose a particular form of dissimilarity space that is adapted to the asymmetric classification problem, and in turn to the query-by-example and relevance feedback paradigm, widely used in information retrieval. Based on the proposed dissimilarity space, we then define various strategies to fuse modalities through a kernel-based learning approach. The problem of automatic kernel setting to adapt the learning process to the queries is also discussed. The properties of our strategies are studied and validated on artificial data. In a second phase, a large annotated video corpus, (ie TRECVID-05), indexed by visual, audio and text features is considered to evaluate the overall performance of the dissimilarity space and fusion strategies. The obtained results confirm the validity of the proposed approach for the representation and retrieval of multimodal information in a real-time framework.

RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina

BackgroundThe retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina.DescriptionRETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed in 5 different model systems: drosophila, zebrafish, rat, mouse and human. The database is supported by a platform that is designed to easily integrate new functionalities and is also frequently updated.ConclusionThe results obtained from various biological scenarios can be visualized, compared and downloaded. The results of a case study are presented that highlight the utility of RETINOBASE. Overall, RETINOBASE provides efficient access to the global expression profiling of retinal genes from different organisms under various conditions.

VitisExpDB: a database resource for grape functional genomics.

BackgroundThe family Vitaceae consists of many different grape species that grow in a range of climatic conditions. In the past few years, several studies have generated functional genomic information on different Vitis species and cultivars, including the European grape vine, Vitis vinifera. Our goal is to develop a comprehensive web data source for Vitaceae.DescriptionVitisExpDB is an online MySQL-PHP driven relational database that houses annotated EST and gene expression data for V. vinifera and non-vinifera grape species and varieties. Currently, the database stores ~320,000 EST sequences derived from 8 species/hybrids, their annotation (BLAST top match) details and Gene Ontology based structured vocabulary. Putative homologs for each EST in other species and varieties along with information on their percent nucleotide identities, phylogenetic relationship and common primers can be retrieved. The database also includes information on probe sequence and annotation features of the high density 60-mer gene expression chip consisting of ~20,000 non-redundant set of ESTs. Finally, the database includes 14 processed global microarray expression profile sets. Data from 12 of these expression profile sets have been mapped onto metabolic pathways. A user-friendly web interface with multiple search indices and extensively hyperlinked result features that permit efficient data retrieval has been developed. Several online bioinformatics tools that interact with the database along with other sequence analysis tools have been added. In addition, users can submit their ESTs to the database.ConclusionThe developed database provides genomic resource to grape community for functional analysis of genes in the collection and for the grape genome annotation and gene function identification. The VitisExpDB database is available through our website .

A Run-time System for Efficient Execution of Scientific Workflows on Distributed Environments

Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a runtime support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components.

DDBJ with new system and face

DDBJ (http://www.ddbj.nig.ac.jp) collected and released 1 880 115 entries or 1 134 086 245 bases in the period from July 2006 to June 2007. The released data contains the high-throughput cDNAs of cricket and high-quality draft genome of medaka among others. Our computer system has been upgraded since March 2007. Another new aspect is an efficient data retrieval tool that has recently been equipped and served at DDBJ. It is called All-round Retrieval for Sequence and Annotation, which enables the user to search for keywords also in the Feature/Qualifier of the International Nucleotide Sequence Database Collaboration (http://www.insdc.org/). We will also replace our home page with a more efficient one by the end of 2007.