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Journal articles on the topic 'Storage and indexing'

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Author: Grafiati

Published: 17 July 2024

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1

Vimal, Vrince. "An Efficient and Secure Query Processing and Indexing model for Secure Dynamic Cloud Storage." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 10, no. 2 (September 10, 2019): 1043–48. http://dx.doi.org/10.17762/turcomat.v10i2.13623.

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To ensure the security and privacy of stored data, as well as the efficacy and efficiency of cloud storage, it is necessary to overcome significant challenges, such as efficient and secure query processing and indexing in dynamic cloud storage. There are a number of limitations with the present methodologies and tactics for query processing and indexing in cloud storage, including high processing overhead, scalability problems, and security concerns. In this paper, we provide a method for efficiently and securely executing queries and indexing data in dynamic cloud storage. The suggested system incorporates scalable indexing techniques, secure query processing, and dynamic data management to overcome these issues. The proposed system has several potential uses in many different areas, including healthcare, finance, e-commerce, government, and research. As new problems arise with cloud storage services, the proposed approach will need to be adjusted and enhanced via ongoing research and development. The proposed method has the potential to enhance data administration and analysis in dynamically managed cloud storage service environments while also protecting data privacy and security.

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Iliopoulos, Costas. "Storage and indexing of massive data." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 372, no. 2016 (May 28, 2014): 20130213. http://dx.doi.org/10.1098/rsta.2013.0213.

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Adeleke, Imran A., Adegbuyi D. Gbadebo, and Abayomi O. Dawodu. "A B+-Tree-Based Indexing and Storage of Numerical Records in School Databases." Asian Journal of Research in Computer Science 16, no. 4 (December 26, 2023): 418–27. http://dx.doi.org/10.9734/ajrcos/2023/v16i4401.

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The need for effective indexing and retrieval of data is paramount in any contemporary organization. However, the use of tree data structure had been effective in this regard as evident in literature. This article gives an overview of B+-tree data structure, its indexing technique and application in indexing and retrieving students’ academic records in the school system in order to make such records flexible. The study demonstrates the indexing and arrangement patterns of some numerical data. In essence, it discusses how to adopt the use of B+-tree data structure to manage some numerical data in order to enhance indexing, retrieval and modifications of such record. It concludes that good record management results in more convenient indexing and retrieval of students’ academic records within the school system.

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Niu, De Jiao, Yong Zhao Zhan, and Tao Cai. "The Multi-Level Metadata Indexing in Mass Storage System." Advanced Materials Research 532-533 (June 2012): 818–22. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.818.

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Metadata query plays an important role in mass storage system. Efficient indexing algorithm can reduce the time and space which greatly determine the efficiency of mass storage system. Typically, temporal and spatial consuming is immense and volatile in the existing metadata management algorithms. In this paper, a novel metadata indexing algorithm is presented. Metadata query algorithm is based on two-level indexing strategy. The metadata is classified into two categories, that are active metadata and non-active metadata. The Bloom Filter is used to generate binary string for active metadata, and the B-tree is used to establish index of each active partition. While, the suitable hash function is selected for each non-active metadata partition. The results show that the multi-level metadata indexing algorithm can reduce the temporal and spatial costs of metadata query.

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Jiang, Chao, Jinlin Wang, and Yang Li. "An Efficient Indexing Scheme for Network Traffic Collection and Retrieval System." Electronics 10, no. 2 (January 15, 2021): 191. http://dx.doi.org/10.3390/electronics10020191.

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Historical network traffic retrieval, both at the packet and flow level, has been applied in many fields of network security, such as network traffic analysis and network forensics. To retrieve specific packets from a vast number of packet traces, it is an effective solution to build indexes for the query attributes. However, it brings challenges of storage consumption and construction time overhead for packet indexing. To address these challenges, we propose an efficient indexing scheme called IndexWM based on the wavelet matrix data structure for packet indexing. Moreover, we design a packet storage format based on the PcapNG format for our network traffic collection and retrieval system, which can speed up the extraction of index data from packet traces. Offline experiments on randomly generated network traffic and actual network traffic are performed to evaluate the performance of the proposed indexing scheme. We choose an open-source and widely used bitmap indexing scheme, FastBit, for comparison. Apart from the native bitmap compression method Word-Aligned Hybrid (WAH), we implement an efficient bitmap compression method Scope-Extended COMPAX (SECOMPAX) in FastBit for performance evaluation. The comparison results show that our scheme outperforms the selected bitmap indexing schemes in terms of time consumption, storage consumption and retrieval efficiency.

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Du, M., J. Wang, C. Jing, J. Jiang, and Q. Chen. "HIERARCHICAL DATA MODEL FOR STORAGE AND INDEXING OF MASSIVE STREET VIEW." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1295–99. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1295-2019.

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<p><strong>Abstract.</strong> Maintaining an up-to-date inventory of urban infrastructure such as fire hydrant is critical to urban management. Street view database such as Google Street View and Baidu Street View contain street-level images, their potential for urban management has not been fully explored. For the massive image, data model for storage and indexing is an important research issue. Considering multiple cameras and GPS device in the image capturing platform, a hierarchical data model named 3D-Grid is proposed. Massive street view images were stored according to grid ID, GPS time and camera ID. An efficient time indexing algorithm is brought forth to replace the spatial indexing. Real test experiments are conducted in a project, and the validation and feasibility of 3D-Grid including time indexing algorithm were validated.</p>

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7

Geetha, K., and A. Vijaya. "Cross-Layer Fragment Indexing based File Deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for Optimized Cloud Storage." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 8s (August 18, 2023): 565–75. http://dx.doi.org/10.17762/ijritcc.v11i8s.7239.

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Cloud computing and storage processing is a big service for maintaining a large number of data in a centralized server to store and retrieve data depending on the use to pay as a service model. Due to increasing storage depending on duplicate copy presence during different sceneries, the increased size leads to increased cost. To resolve this problem, we propose a Cross-Layer Fragment Indexing (CLFI) based file deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for optimized cloud storage. Initially, the file storage indexing easy carried out with Lexical Syntactic Parser (LSP) to split the files into blocks. Then comparativesector was created based on Chunk staking. Based on the file frequency weight, the relative Indexing was verified through Cross-Layer Fragment Indexing (CLFI). Then the fragmented index gets grouped by maximum relative threshold margin usingIntra Subset Near-Duplicate Clusters (ISNDC). The hashing is applied to get comparative index points based on hyper correlation comparer using Hyper Spectral Hash Duplicate Filter (HSHDF). This filter the near duplicate contentdepending on file content difference to identify the duplicates. This proposed system produces high performance compared to the other system. This optimizes cloudstorage and has a higher precision rate than other methods.

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Moses, Timothy, Abubakar Usman Othman, Umar Yahaya Aisha, Abdulsalam Ya’u Gital, Boukari Souley, and Badmos Tajudeen Adeleke. "Big data indexing: Taxonomy, performance evaluation, challenges and research opportunities." Journal of Computer Science and Engineering (JCSE) 3, no. 2 (September 6, 2022): 71–94. http://dx.doi.org/10.36596/jcse.v3i2.548.

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In order to efficiently retrieve information from highly huge and complicated datasets with dispersed storage in cloud computing, indexing methods are continually used on big data. Big data has grown quickly due to the accessibility of internet connection, mobile devices like smartphones and tablets, body-sensor devices, and cloud applications. Big data indexing has a variety of problems as a result of the expansion of big data, which is seen in the healthcare industry, manufacturing, sciences, commerce, social networks, and agriculture. Due to their high storage and processing requirements, current indexing approaches fall short of meeting the needs of large data in cloud computing. To fulfil the indexing requirements for large data, an effective index strategy is necessary. This paper presents the state-of-the-art indexing techniques for big data currently being proposed, identifies the problems these techniques and big data are currently facing, and outlines some future directions for research on big data indexing in cloud computing. It also compares the performance taxonomy of these techniques based on mean average precision and precision-recall rate.

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9

Turner, James M. "Indexing “Ordinary” Pictures for Storage and Retrieval." Visual Resources 10, no. 3 (January 1994): 265–73. http://dx.doi.org/10.1080/01973762.1994.9658292.

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10

Kundu, Anirban, Siddhartha Sett, Subhajit Kumar, Shruti Sengupta, and Srayan Chaudhury. "Search engine indexing storage optimisation using Hamming distance." International Journal of Intelligent Information and Database Systems 6, no. 2 (2012): 113. http://dx.doi.org/10.1504/ijiids.2012.045845.

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Mathis, Christian, Theo Härder, Karsten Schmidt, and Sebastian Bächle. "XML indexing and storage: fulfilling the wish list." Computer Science - Research and Development 30, no. 1 (February 1, 2012): 51–68. http://dx.doi.org/10.1007/s00450-012-0204-6.

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12

Chen, S., Z. Wang, L. Bai, K. Liu, J. Gao, M. Zhao, and M. D. Mulvenna. "LARGE VECTOR SPATIAL DATA STORAGE AND QUERY PROCESSING USING CLICKHOUSE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-1-2023 (April 21, 2023): 65–72. http://dx.doi.org/10.5194/isprs-archives-xlviii-m-1-2023-65-2023.

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Abstract. The exponential growth of geospatial data resulting from the development of earth observation technology has created significant challenges for traditional relational databases. While NoSQL databases based on distributed file systems can handle massive data storage, they often struggle to cope with real-time query. Column-storage databases, on other hand, are highly effective at both storage and query processing for large-scale datasets. In this paper, we propose a spatial version of ClickHouse that leverages R-Tree indexing to enable efficient storage and real-time analysis of massive remote sensing data. ClickHouse is a column-oriented, open-source database management system designed for handling large-scale datasets. By integrating R-Tree indexing, we have created a highly efficient system for storing and querying geospatial data. To evaluate the performance of our system, we compare it with HBase, a popular distributed, NoSQL database system. Our experimental results show that ClickHouse outperforms HBase in handling spatial data queries, with a response time approximately three times faster than HBase. We attribute this performance gain to the highly efficient R-Tree indexing used in ClickHouse, which allows for fast spatial data query.

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13

Tang, Hong Jie. "Study of XML Indexing Structure Based on XISS." Applied Mechanics and Materials 851 (August 2016): 611–14. http://dx.doi.org/10.4028/www.scientific.net/amm.851.611.

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The study is based on XISS(XML Indexing and Storage System) of Dietz’s Numbering Schema to determine the ancestor-descendant relationship. According to the results of research, this paper proposes an improved method of node encoding, realizes its indexing structure, and discusses its query path. Finally, the paper analyzes the property of this improved method.

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Li, Wu, Wu, and Zhao. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11 (November 12, 2019): 512. http://dx.doi.org/10.3390/ijgi8110512.

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Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

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Kouahla, Zineddine, Ala-Eddine Benrazek, Mohamed Amine Ferrag, Brahim Farou, Hamid Seridi, Muhammet Kurulay, Adeel Anjum, and Alia Asheralieva. "A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues." Future Internet 14, no. 1 (December 31, 2021): 19. http://dx.doi.org/10.3390/fi14010019.

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The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.

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16

Yang, Yuqi, Xiaoqing Zuo, Kang Zhao, and Yongfa Li. "Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data." ISPRS International Journal of Geo-Information 13, no. 6 (June 12, 2024): 197. http://dx.doi.org/10.3390/ijgi13060197.

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a service basis, especially when applied to massive trajectory data. In this study, we propose a massive GPS data storage and indexing method based on uneven spatial segmentation and trajectory optimization segmentation. Primarily, the method divides GPS trajectories in a large spatio-temporal data space into multiple MBR sequences by greedy algorithm. Then, a hybrid indexing model for segmented trajectories is constructed to form a global spatio-temporal segmentation scheme, called HHBITS index, to achieve hierarchical organization of trajectory data. Eventually, a spatio-temporal range query processing method is proposed based on this index. This paper implements and evaluates the index in MongoDB and compares it with two other spatio-temporal composite indexes for performing spatio-temporal range queries efficiently. The experimental results show that the method in this paper has high performance in responding to spatio-temporal queries on large-scale trajectory data.

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17

Al-Mouhamed, M. "An efficient indexing scheme for image storage and recognition." IEEE Transactions on Industrial Electronics 46, no. 2 (April 1999): 429–39. http://dx.doi.org/10.1109/41.753782.

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18

Wang, Sheng, David Maier, and Beng Chin Ooi. "Lightweight indexing of observational data in log-structured storage." Proceedings of the VLDB Endowment 7, no. 7 (March 2014): 529–40. http://dx.doi.org/10.14778/2732286.2732290.

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19

Shin, Dongwook. "XML Indexing and Retrieval with a Hybrid Storage Model." Knowledge and Information Systems 3, no. 2 (May 2001): 252–61. http://dx.doi.org/10.1007/pl00011668.

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20

Ghani, Abdul. "Arabic literature: Uniterm indexing system for storage and retrieval." International Library Review 19, no. 4 (October 1987): 321–33. http://dx.doi.org/10.1016/0020-7837(87)90043-4.

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21

Ren, Shu Qin, Benjamin Hong Meng Tan, Sivaraman Sundaram, Taining Wang, Yibin Ng, Victor Chang, and Khin Mi Mi Aung. "Secure searching on cloud storage enhanced by homomorphic indexing." Future Generation Computer Systems 65 (December 2016): 102–10. http://dx.doi.org/10.1016/j.future.2016.03.013.

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22

Singhal, Shubhanshi, Akanksha Kaushik, and Pooja Sharma. "A Novel approach of data deduplication for distributed storage." International Journal of Engineering & Technology 7, no. 2.4 (March 10, 2018): 46. http://dx.doi.org/10.14419/ijet.v7i2.4.10040.

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Due to drastic growth of digital data, data deduplication has become a standard component of modern backup systems. It reduces data redundancy, saves storage space, and simplifies the management of data chunks. This process is performed in three steps: chunking, fingerprinting, and indexing of fingerprints. In chunking, data files are divided into the chunks and the chunk boundary is decided by the value of the divisor. For each chunk, a unique identifying value is computed using a hash signature (i.e. MD-5, SHA-1, SHA-256), known as fingerprint. At last, these fingerprints are stored in the index to detect redundant chunks means chunks having the same fingerprint values. In chunking, the chunk size is an important factor that should be optimal for better performance of deduplication system. Genetic algorithm (GA) is gaining much popularity and can be applied to find the best value of the divisor. Secondly, indexing also enhances the performance of the system by reducing the search time. Binary search tree (BST) based indexing has the time complexity of which is minimum among the searching algorithm. A new model is proposed by associating GA to find the value of the divisor. It is the first attempt when GA is applied in the field of data deduplication. The second improvement in the proposed system is that BST index tree is applied to index the fingerprints. The performance of the proposed system is evaluated on VMDK, Linux, and Quanto datasets and a good improvement is achieved in deduplication ratio.

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Aouat, Saliha, and Slimane Larabi. "Object Retrieval Using the Quad-Tree Decomposition." Journal of Intelligent Systems 23, no. 1 (January 1, 2014): 33–47. http://dx.doi.org/10.1515/jisys-2013-0014.

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AbstractWe propose in this article an indexing and retrieval approach applied on outline shapes. Models of objects are stored in a database using the textual descriptors of their silhouettes. We extract from the textual description a set of efficient similarity measures to index the silhouettes. The extracted features are the geometric quasi-invariants that vary slightly with the small change in the viewpoint. We use a textual description and quasi-invariant features to minimize the storage space and to achieve an efficient indexing process. We also use the quad-tree structure to improve processing time during indexing. Using both geometric features and quad-tree decomposition facilitates recognition and retrieval processes. Our approach is applied on the outline shapes of three-dimensional objects. Experiments conducted on two well-known databases show the efficiency of our method in real-world applications, especially for image indexing and retrieval.

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24

YAO, Qiu-Lin, Ying WANG, Ping LIU, and Li GUO. "Storage Optimized Containment-Encoded Intervals Indexing for Data Stream Querying." Journal of Software 20, no. 9 (November 13, 2009): 2462–69. http://dx.doi.org/10.3724/sp.j.1001.2009.03402.

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25

Renugha, K., P. Shanthi, and A. Umamakeswari. "Multi-Keyword Ranked Search in Cloud Storage using Homomorphic Indexing." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 243. http://dx.doi.org/10.14419/ijet.v7i2.24.12057.

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In the cloud environment, the main issue is outsourcing of the information to the cloud service provider and outsider. Consider this, the cloud tenant store data in an encrypted form to achieve data security and privacy. The data owner needs the secure information sharing from the cloud and without leak of access pattern to the eavesdroppers. XOR homomorphic encryption searchable algorithm along with ranking is proposed to provide the security over the network. In addition our scheme provides secure Multi-keyword ranked search over encrypted data. Efficient ranked search algorithm returns the relevant document based on the results for the given multiple keywords. The experimental results prove that the system is efficient.

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26

Gibson, Seann, and Kerr Gibson. "Mtree data structure for storage, indexing and retrieval of information." Laboratory Automation & Information Management 33, no. 1 (June 1997): 64. http://dx.doi.org/10.1016/s1381-141x(97)80054-8.

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S, Karthi, and Prabu S. "Execution Analysis of Spatial Data Storage Indexing on Cloud Environment." Scalable Computing: Practice and Experience 19, no. 4 (December 29, 2018): 339–49. http://dx.doi.org/10.12694/scpe.v19i4.1421.

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Cloud computing overcome the GIS issues are huge storage, computing and reliability. Cloud computing with SpatialHadoop framework gives high performance in GIS. This paper presents spatial partition, global index and map reduce operations were studied and described in detail. Bloom filter R-tree index in the Map-reduce for providing more efficiency than the existing approaches. The BR-tree index on Map-Reduce is implemented in SpatialHadoop process that reduces intermediate data access time. Global index decreases the number of data accesses for range queries and thus improves efficiency. It is observed through experimental results that the proposed index along cloud environment performs better than existing techniques

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Tyagi, Nidhi, Rahul Rishi, and R. P. Agarwal. "Context based Web Indexing for Storage of Relevant Web Pages." International Journal of Computer Applications 40, no. 3 (February 29, 2012): 1–5. http://dx.doi.org/10.5120/5021-7166.

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29

Bansode, Monika. "A survey on Content Based Image Retrieval in Cloud Environment with Privacy Preservation & Copy Deterrence." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 2745–49. http://dx.doi.org/10.22214/ijraset.2021.37852.

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Abstract: Importance of images in day to day life increased tremendously. Therefore Content Based Image Retrieval studied extensively. Cloud computing offers on demand services to cloud user therefore many organizations prefer to use cloud for data storage. To protect images with sensitive or private information needs to be encrypted before being outsourced to cloud. However, this causes difficulties in image retrieval and management. The purpose of this study is to provide privacy preservation and copy deterrence Content Based Image Retrieval method using Lucene Indexing. Keywords: CBIR, Lucene Indexing, Copy Deterrence.

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Jiau, Hewijin Christine, and Chuan-Wang Chang. "A Dual Ternary Indexing Approach for Music Retrieval System." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 3 (May 20, 2008): 227–33. http://dx.doi.org/10.20965/jaciii.2008.p0227.

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Memory usage for storing indexes and query response times for retrieval processing are two critical issues in music information retrieval (MIR) systems. In this paper, we propose an effective and efficient numeric indexing structure to overcome the difficulties of variable length queries and enhance the efficiency of music retrieval. The proposed structure differs greatly from pre-existing research in textual indexing techniques such asn-gram and suffix tree because it does not need to generate redundant and useless indexes. The index construction process has no complicated split and joint operations making, is easier and faster than tree-like methods. Experimental results show that our method is more scalable and economical than previous methods. The proposed method can significantly reduce the processing time and storage for retrieving and indexing.

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Srisungsittisunti, Bowonsak, Jirawat Duangkaew, Sakorn Mekruksavanich, Nakarin Chaikaew, and Pornthep Rojanavasu. "Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (June 1, 2024): 2342. http://dx.doi.org/10.11591/ijai.v13.i2.pp2342-2353.

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<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper"><div class="header-controls"><span>The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.</span></div></div></div>

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CHAROPOULOS, C., F. ANDRITSOPOULOS, Y. MITSOS, G. DOUMENIS, and G. STASINOPOULOS. "PACKET INDEXING PROCESS OPTIMIZED FOR HIGH-SPEED NETWORK PROCESSORS." Journal of Circuits, Systems and Computers 14, no. 04 (August 2005): 841–60. http://dx.doi.org/10.1142/s0218126605002623.

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Most network processors perform some kind of classification on the received packet stream, according to criteria set by the implemented networking application. Packet indexing is an integral part of the packet classification process. Indexing is considered as one of the most processor intensive part of network processing and is often supported by special hardware units. High performance Network processors usually rely upon Content Addressable Memories (CAMs) for the indexing of millions of packets per second into discrete "flow Identifiers" in ATM and IP networks. Most often, the indexing process examines packet data (tags) of significant size, necessitating the use of large CAM devices. This paper proposes an alternative method for searching lengthy tags, using RAM as storage medium instead of the expensive and complex CAMs. The technique applies the open-addressing hashing methodology to provide high speed lookups, close to CAM's performance. Our approach handles efficiently the limitations imposed by the hashing algorithms by appropriately selecting system parameters and resolving hashing collisions. The advantages of the proposed method are evaluated in detail.

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33

Jkr Sastry, Dr, Chandu Sai Chittibomma, and Thulasi Manohara Reddy Alla. "Enhancing the performance of search engines based heap based data file and hash based indexing file." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 372. http://dx.doi.org/10.14419/ijet.v7i2.7.10722.

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WEB clients use the WEB for searching the content that they are looking for through inputting keywords or snippets as input to the search engines. Search Engines follows a process to collect the content and provide the same as output in terms of URL links. Sometimes enormous time is taken to fetch the content fetched especially when it goes into number of display pages. Locating the content among the number of pages of URLS displayed is complex. Proper indexing method will help in reducing the number of display pages and enhances the seed of processing and result into reducing the size of index space.In this paper a non-clustered indexing method based on hash based indexing and when the data is stored as a heap file is presented that helps the entire search process quite fast requiring very less storage area.

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Zhao, Xiu Mei, Fang Ai Liu, and Song Qin. "Chord-Based Indexing Model to Support Complex Query and Load Balancing." Key Engineering Materials 474-476 (April 2011): 1781–86. http://dx.doi.org/10.4028/www.scientific.net/kem.474-476.1781.

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This paper presents a new model (SMF-Chord) which is expanded from Chord. SMF-Chord uses double-fingerprint, double-mapping and dynamic forwarding mechanism to support multi-attribute multi-keyword fuzzy-matching query, and also has a load balancing mechanism which includes three parts: similar-close transposition, forward balancing and hot-set cache. The experiment results show SMF-Chord has high recall rate with low storage redundancy, and it can effectively balance load when node mapping load, file storage load, or query load is unbalanced.

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Simeonov, George, and Peter Stanchev. "Using Dspace Platform for Creation of Open Access Local Repositories." Digital Presentation and Preservation of Cultural and Scientific Heritage 9 (September 13, 2019): 245–50. http://dx.doi.org/10.55630/dipp.2019.9.23.

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The paper presents a brief review of the Dspace software platform for long-term data storage with indexing and search system used for open access repository creation. The experience of using and maintaining the platform for building BulDML and BGOpenAIRE repositories are highlighted.

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Khettabi, K., B. Farou, Z. Kouahla, and H. Seridi. "Efficient Storage of Heterogeneous IoT data in a Blockchain using an Indexing Method in Metric Space." International Journal on Cybernetics & Informatics 13, no. 2 (March 10, 2024): 125–33. http://dx.doi.org/10.5121/ijci.2024.130209.

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In this work, we proposed a IoT data indexing method to surpass some challenges encountered during the use of hashing in the storage of data in a blockchain. The indexing method was developed in metric space in which no dimensions are considered and only distance between objects is taken into account. The proposed method consisted on putting the index in the inner of a block. The index, called GHB-tree is based on space partitioning using hyperplane. The proposed approach was tested using two datasets of close size and different dimensions. The experimental results showed that the proposed method is efficient and competitive to other storing methods since the queries retrieve time is very reduced to be expressed by millisecond compared with that of other blockchains.

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Tulkinbekov, Khikmatullo, and Deok-Hwan Kim. "Data Modifications in Blockchain Architecture for Big-Data Processing." Sensors 23, no. 21 (October 27, 2023): 8762. http://dx.doi.org/10.3390/s23218762.

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Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%.

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Krommyda, Maria, and Verena Kantere. "Spatial Data Management in IoT Systems: Solutions and Evaluation." International Journal of Semantic Computing 15, no. 01 (March 2021): 117–39. http://dx.doi.org/10.1142/s1793351x21300016.

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As the Internet of Things (IoT) systems gain in popularity, an increasing number of Big Data sources are available. Ranging from small sensor networks designed for household use to large fully automated industrial environments, the IoT systems create billions of measurements each second making traditional storage and indexing solutions obsolete. While research around Big Data has focused on scalable solutions that can support the datasets produced by these systems, the focus has been mainly on managing the volume and velocity of these data, rather than providing efficient solutions for their retrieval and analysis. A key characteristic of these data, which is, more often than not, overlooked, is the spatial information that can be used to integrate data from multiple sources and conduct multi-dimensional analysis of the collected information. We present here the solutions currently available for the storage and indexing of spatial datasets produced by the IoT systems and we discuss their applicability in real-world scenarios.

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Murali, Pranav. "An Approach to Trie Based Keyword Search for Search Engines." International Journal of Library and Information Services 6, no. 1 (January 2017): 1–16. http://dx.doi.org/10.4018/ijlis.2017010101.

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Search Engines use indexing techniques to minimize the time taken to find the relevant information to a search query. They maintain a keywords list that may reside either in the memory or in the external storage, like a hard disk. While a pure binary search can be used for this purpose, it suffers from performance issue when keywords are stored in the external storage. Some implementations of search engines use a B-tree and sparse indexes to reduce access time. This paper aims at reducing the keyword access time further. It presents a keyword search technique that utilizes a combination of trie data structure and a new keyword prefixing method. Experimental results show good improvement in performance over pure binary search. The merits of incorporating trie based approach into contemporary indexing methods is also discussed. Keyword prefixing method is described and some salient steps in the process of keyword generation are outlined.

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Sreelatha, Kakunuri, and Vuyyuru Krishna Reddy. "Integrity and memory consumption aware electronic health record handling in cloud." Concurrent Engineering 29, no. 3 (July 2, 2021): 258–65. http://dx.doi.org/10.1177/1063293x211027869.

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Cloud environment greatly necessitates two key factors namely integrity and memory consumption. In the proposed work, an efficient integrity check system (EICS) is presented for electronic health record (EHR) classification. The existing system does not concentrate on storage concerns such as storing and retrieving files in cloud and memory storage overheads. De-duplication is one of the solution, however original information loss might take place. This is mitigated by the suggested research work namely Integrity and Memory Consumption aware De-duplication Method (IMCDM), where health care files are stored in secured and reliable manner. File Indexed table are created for all the files for enhancing de-duplication performance before uploading it into server. Duplication existence can be obtained from the indexing table which comprises of file features and hash values. Support vector machine (SVM) classifier is used in indexing table construction for file feature learning. Labels allotted through SVM classifier is considered as index values. Two level encryption is used followed by indexing construction, and stored in cloud severs. For avoiding redundant data, a decrypted hash index comparison is performed with previously stored contents. Various security key based on individual user’s generation is carried for ensuring security and XOR operation is performed with received encrypted file. The evaluation is performed using the Java simulation tool, which aids in validating the proposed methodology against existing research.

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Al-Omoush, Ashraf, Norita Md Norwawi, and Ahmad Akmalludin Mazlan. "Handling Words Duplication and Memory Management for Digital Quran Based on Hexadecimal Representation and Sparse Matrix." International Journal of Engineering & Technology 7, no. 4.15 (October 7, 2018): 481. http://dx.doi.org/10.14419/ijet.v7i4.15.25760.

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Al-Quran is the holy book of the Muslims and the most important scripture containing knowledge on many domains. The recent advent of smart technologies like smart phones, digital devices and tablets has connected the daily life routines under a single touch adopted by many, these new tools with an exponential growth. This paper presented a Digital Quran Model (DQM) using hexadecimal representation using Unicode Hexadecimal and UTF-8 for character encoding, which is backward compatible with ASCII code. DQM target to handle all duplicated words or verses in Al-Quran using sparse matrix with double offset indexing to handle memory optimization. Three approaches were discussed: indexing and representation of the digital Quran to optimize storage, organize verses structure using sparse matrix to handle repetition with double offset indexing to efficiently use the space. The algorithms were implemented using Visual studio and Java server and the solution quality was measured by the size of a file before and after applying DQM model. For surah Al-Baqarah, the longest chapter in the Al-Quran, the reduction of the storage size was 25.00% whereas surah Al-Fatihah was 47.89%. The proposed DQM model is able to optimize the memory space and can be extended to other non-Roman characters used for information retrieval such as Hindi, Chinese and Japanese that are categorized in unicode standards.

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Leung, A. W., M. Shao, T. Bisson, S. Pasupathy, and E. L. Miller. "High-performance metadata indexing and search in petascale data storage systems." Journal of Physics: Conference Series 125 (July 1, 2008): 012069. http://dx.doi.org/10.1088/1742-6596/125/1/012069.

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Wang, Sheng, Xiaolin Qin, Zhifeng Bao, and Bohan Li. "Tide-tree: A self-tuning indexing scheme for hybrid storage system." World Wide Web 20, no. 5 (December 21, 2016): 1017–45. http://dx.doi.org/10.1007/s11280-016-0426-9.

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Chawla, Tanvi, Girdhari Singh, Emmanuel S. Pilli, and M. C. Govil. "Storage, partitioning, indexing and retrieval in Big RDF frameworks: A survey." Computer Science Review 38 (November 2020): 100309. http://dx.doi.org/10.1016/j.cosrev.2020.100309.

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Doan, Quang-Tu, A. S. M. Kayes, Wenny Rahayu, and Kinh Nguyen. "Integration of IoT Streaming Data With Efficient Indexing and Storage Optimization." IEEE Access 8 (2020): 47456–67. http://dx.doi.org/10.1109/access.2020.2980006.

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Du, Minxin, Qian Wang, Meiqi He, and Jian Weng. "Privacy-Preserving Indexing and Query Processing for Secure Dynamic Cloud Storage." IEEE Transactions on Information Forensics and Security 13, no. 9 (September 2018): 2320–32. http://dx.doi.org/10.1109/tifs.2018.2818651.

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Kim, Jin-Seung, Yong-Koo Han, and Young-Koo Lee. "Efficient Storage and Retrieval for Automatic Indexing of Persons in Videos." Journal of Korea Multimedia Society 14, no. 8 (August 31, 2011): 1050–60. http://dx.doi.org/10.9717/kmms.2011.14.8.1050.

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Putri, Yessy Prima, and Ridwan Lawson. "Aplikasi Pengkoreksi Kesalahan Ejaan dan Padanan Kata pada Tugas Akhir Mahasiswa." Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer 14, no. 2 (September 20, 2019): 72. http://dx.doi.org/10.30872/jim.v14i2.1811.

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Pada proses pengerjaan tugas akhir atau skripsi, mahasiswa STMIK Indonesia Padang sering sekali melakukan kesalahan penulisan dalam hal pengetikan dan pengetahuan yang kurang terhadap penulisan ejaan dan padanan kata yang paling update dan sesuai dengan KBBI. Kesalahan yang sering terjadi adalah kurangnya pengetahuan mahasiswa akan penulisan ejaan yang baku, kelalaian mahasiswa yang tidak disengaja, kesalahan pengaturan aplikasi yang digunakan untuk media pengetikan (Microsoft Word, Notepad, Open Office Word) dan beberapa hal lainnya. Aplikasi deteksi kesalahan penulisan skripsi merupakan solusi untuk membantu mahasiswa dalam membuat skripsi dan mendeteksi kesalahan penulisan dokumen skripsi. Salah satu metode indexing untuk meng-indeks teks biasa, untuk mengurangi kapasitas pemakaian storage dan meningkatkan kinerja searching adalah Full Text Indexing. Full Text Indexing merupakan metode yang digunakan dalam mencari kesalahan dalam sebuah teks sebagai alat bantu utama dalam perancangan aplikasi ini. Pada metode Full Text Indexing terdapat 2 tahap yang dilakukan sebelum dilakukan pencarian kata, yaitu tahap tokenizing dan tahap cleansing. Aplikasi deteksi kesalahan penulisan naskah dokumen skripsi dibuat dengan fitur pengecekan kesalahan penulisan dan penyimpanan daftar pustaka dan daftar gambar. Dengan dibuatnya aplikasi ini diharapkan bisa membantu mahasiswa dalam pembuatan skripsi, terutama dalam pengecekan kesalahan penulisan skripsi.

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Guan, Runda, Ziyu Wang, Xiaokang Pan, Rongjie Zhu, Biao Song, and Xinchang Zhang. "SbMBR Tree—A Spatiotemporal Data Indexing and Compression Algorithm for Data Analysis and Mining." Applied Sciences 13, no. 19 (September 22, 2023): 10562. http://dx.doi.org/10.3390/app131910562.

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In the field of data analysis and mining, adopting efficient data indexing and compression techniques to spatiotemporal data can significantly reduce computational and storage overhead for the abilities to control the volume of data and exploit the spatiotemporal characteristics. However, traditional lossy compression techniques are hardly suitable due to their inherently random nature. They often impose unpredictable damage to scientific data, which affects the results of data mining and analysis tasks that require certain precision. In this paper, we propose a similarity-based minimum bounding rectangle (SbMBR) tree, a tree-based indexing and compression method, to address the aforementioned problem. Our method can hierarchically select appropriate minimum bounding rectangles according to the given maximum acceptable errors and use the average value contained in each selected MBR to replace the original data to achieve data compression with multi-layer loss control. This paper also provides the corresponding tree construction algorithm and range query processing algorithm for the indexing structure mentioned above. To evaluate the data quality preservation in cross-domain data analysis and mining scenarios, we use mutual information as the estimation metric. Experimental results emphasize the superiority of our method over some of the typical indexing and compression algorithms.

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Song, Xiao Yu, Yong Hui Wang, and Shou Jin Wang. "The Study and Design of QR*-Tree Spatial Indexing Structure." Applied Mechanics and Materials 182-183 (June 2012): 2030–34. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.2030.

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In this study, we will discuss a fast spatial indexing structure called QR*-tree based on R*-tree and quad-tree. Now, R*-tree and R-tree are widely used in spatial database as a spatial indexing structure, But for each algorithm alone, it is not suitable for the huge data volume. The hybrid structure that we proposed is composed of many R*-trees based on space partitioned by quad-tree. Although it demands more storage space than R*-tree or quad-tree, it gains better performance in insertion, deletion, and searching especially, and the more the amount of spatial data is, the better performance the hybrid-tree has.

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