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ACM International Conference on Multimedia Retrieval (ICMR)

作者： Mourao, Andre Magalhaes, Joao Univ Nova Lisboa NOVA LINCS Lisbon Portugal

ISBN: (纸本)9781450367653

Distributing multimedia indexes to multiple nodes enables search over very large datasets (i.e., over one billion images and videos), but comes with a set of challenges: how to distribute documents and queries efectively across nodes to support concurrent querying? and how to deal with the increased potential for lack of response from nodes (e.g., node fail-stops or dropping of network packages)? An index where partitions are based on the distribution of feature vectors in the original space can improve redundancy and increase eiciency: nearest neighbors are only present on a small, set number of partitions, reducing the number of nodes to inspect for each query. This paper describes how sparse hashes can help ind this balance and create better distribution policies for high-dimensional feature vectors. Inspired by existing literature on distributed text and media indexes, our proposal distributes and balances documents and queries to a subset of the nodes, according to their orthogonal similarities. We performed exhaustive benchmarks of our approach on a commercial cloud service. Experiments on a one billion vector dataset show that our approach has a low partitioning overhead (3 to 5 ms per query), achieves balanced document and query distribution (the variation in document and query distribution across nodes is smaller than 1% and 10%, respectively), handles concurrent queries efectively and degrades gracefully with node failures (less than 2% of precision loss per node down).

关键词： distributed indexing redundant partitioning load balancing

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In-Memory distributed indexing for Large-Scale Media Data Retrieval 19

In-Memory Distributed Indexing for Large-Scale Media Data Re...

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19th IEEE International Symposium on Multimedia (ISM)

作者： Ma, Yinmiao Liu, Danlu Scott, Grant Uhlmann, Jeffrey Shyu, Chi-Ren Univ Missouri Elect Engn & Comp Sci Dept Columbia MO 65211 USA Univ Missouri Inst Informat Columbia MO 65211 USA Asia Univ Bioinformat & Biomed Engn Taichung Taiwan

ISBN: (纸本)9781538629376

Data retrieval serves a critical role in the development of multimedia applications. However, due to the exponential growth of multimedia data, high-speed and efficient indexing is becoming more and more difficult than ever. In this paper, we propose a novel approach to speed up the retrieval process by adopting a distributed computing paradigm through the Apache Spark framework Utilizing search trees in a Big Data ecosystem leads to fast and cost-effective media database retrievals by caching indexing structures into memory and aggregating ranked results with flexibilities for users to specify the importance of search cues. We conducted computational experiments on large-scaled vector files for remote sensing image database and synthesized pollen image database to demonstrate the effectiveness and scalability of our system with reasonably high accuracy.

关键词： In-Memory Computing Big Data distributed indexing Image Database Retrieval

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A distributed In-network indexing Mechanism for the Internet of Things 3

A Distributed In-network Indexing Mechanism for the Internet...

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IEEE 3rd World Forum on Internet of Things (WF-IoT)

作者： Fathy, Yasmin Barnaghi, Payam Enshaeifar, Shirin Tafazolli, Rahim Univ Surrey Elect Engn Dept ICS Guildford GU2 7XH Surrey England

ISBN: (纸本)9781509041305

The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system;however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.

关键词： Internet of Things (IoT) Wireless Sensor Networks (WSN) distributed indexing real-world data

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A distributed in-network indexing mechanism for the Internet of Things

A distributed in-network indexing mechanism for the Internet...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Yasmin Fathy Payam Barnaghi Shirin Enshaeifar Rahim Tafazolli Electronic Engineering Department University of Surrey Guildford Surrey United Kingdom

ISBN: (纸本)9781509041312

The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system; however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.

关键词： real-world data Internet of Things (IoT) Wireless Sensor Networks (WSN) distributed indexing

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Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

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Computer Modeling in Engineering & Sciences 2023年第2期134卷 783-815页

作者： Bao Rong Chang Hsiu-Fen Tsai Yu-Chieh Lin Department of Computer Science and Information Engineering National University of KaohsiungKaohsiungTaiwan Department of Fragrance and Cosmetic Science Kaohsiung Medical UniversityKaohsiungTaiwan

Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system *** paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput ***,integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing,which reduces the search scope of the database and dramatically speeds up data ***,exploiting a deep neural network to predict the approximate execution time of a job gives prioritized job scheduling based on the shortest job first,which reduces the average waiting time of job *** a result,the proposed data retrieval approach outperforms the previous method using a deep autoencoder and Solr indexing,significantly improving the speed of data retrieval up to 53%and increasing system throughput by 53%.On the other hand,the proposed job scheduling algorithmdefeats both first-in-first-out andmemory-sensitive heterogeneous early finish time scheduling algorithms,effectively shortening the average waiting time up to 5%and average weighted turnaround time by 19%,respectively.

关键词： Stacked sparse autoencoder Elasticsearch distributed indexing data retrieval deep neural network job scheduling

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Scalable Time Series Compound Infrastructure 22

Scalable Time Series Compound Infrastructure

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International Conference on Management of Data (SIGMOD)

作者： Alghamdi, Noura S. Zhang, Liang Rundensteiner, Elke A. Eltabakh, Mohamed Y. Worcester Polytech Inst WPI Worcester MA 01609 USA

ISBN: (纸本)9781450392495

Objects ranging from a patient's history of medical tests to an IoT device's series of sensor maintenance records leave digital traces in the form of big time series. These time series objects do not only span exceedingly long time periods (sometimes years), but are also characterized by intermittent yet interrelated time series measurements punctuated by long gaps of silence. This prevalent data type, which we refer to as Time Series Compound objects (or, TSC), has been largely overlooked in the literature. Unique challenges arise when managing, querying and analyzing repositories of these big TSC objects. These include appropriate similarity semantics with time misalignment resiliency, efficient storage of excessively long and complex objects, and TSC-holistic indexing. We demonstrate that state-of-the-art time series systems, although effective at indexing and searching regular time series data, fail to support such big TSC data. In this work, we introduce the first comprehensive solution for managing TSC objects as first class citizen. We introduce new similarity-match semantics as well as a compact misalignment-resilient representation for TSCs. Upon this foundation, we then design a TSC-aware distributed indexing infrastructure Sloth that supports scalable storage, indexing and querying of TB-scale TSC datasets. Our experimental study demonstrates that for TB-scale datasets, the query response time of Sloth is up to one order of magnitude faster than that of existing systems, while the mean average precision (mAP) for approximate kNN similarity match query results by Sloth is 70% more accurate than existing solutions.

关键词： Time Series Compound distributed indexing Similarity Search kNN Approximate Query Sloth

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Exploring Multimedia Movement Through Spatio-Temporal indexing and Double-Cache Schemes

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 530-536页

作者： Qin, Zhen Zhang, Lin China Univ Petr East China Coll Mech & Elect Engn Qingdao 266000 Peoples R China Shandong Univ Sci & Technol Coll Art Qingdao 266000 Peoples R China

-Conventional IP/TCP designs encounter several safety and scalability concerns with the growing demand for application services. A novel Internet design, like a Content Center Network (CCN), was introduced to address these issues comprehensively. Every hub within a CCN is responsible for data storage. The collaboration guarantees users quick data retrieval. By collaborating with dual caches, network peers can access data from their caches and leverage other peers' caches, resulting in improved cache utilization and overall network speed. The present study examines multimodal digital artworks' form, style, and action relationships and views them as holistic creative units. The study examines the complex structure of digital content following current information. We present a distributed index incorporating spatio-temporal information to address the challenges of storing and retrieving large amounts of spatio-temporal data. This distributed index combines internal R with external B+ trees to provide high concurrency and low latency indexing services for external applications. With double buffer technology and distributed index architecture, we can optimize the cache utility of content center networks and enhance the retrieval speed of multimedia data. Adopting the distributed index, designed to accommodate spatio-temporal data in the multimedia digital art design, can enhance large-scale storage and retrieval for Internet- future architectures.

关键词： -Multimedia digital art double-cache collaboration distributed indexing spatio-temporal data content center network

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Scalable Decentralized indexing and Querying of Multi-Streams in the Fog

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JOURNAL OF GRID COMPUTING 2020年第3期18卷 395-418页

作者： Dazzi, Patrizio Mordacchini, Matteo ISTI CNR Pisa Italy IIT CNR Pisa Italy

NOA-AID (Network Overlays for Adaptive information Aggregation, indexing and Discovery on the fog) is an approach for decentralized indexing, aggregation and discovery of data belonging to streams. It is organized on two network layers. The upper layer is in charge of delivering an information discovery approach by providing a distributed index structure. The lower layer is devoted to resource aggregation based on epidemic protocols designed for highly dynamic environment, well suited to stream-oriented scenarios. It defines a flexible approach to express queries targeting highly heterogeneous data, as well as a self-organizing dynamic system allowing the optimal resolution of queries on the most suitable stream producers. The paper also presents a theoretical study and discusses the costs related to information management operations;it also gives an empirical validation of findings. Finally, it reports an extended experimental evaluation that demonstrated the ability of NOA-AID to be effective and efficient for retrieving information extracted from streams in highly-dynamic and distributed processing architectures.

关键词： Streaming Network protocols distributed indexing Community detection

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Big Data Series Analytics Using TARDIS and its Exploitation in Geospatial Applications 20

Big Data Series Analytics Using TARDIS and its Exploitation ...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Zhang, Liang Alghamdi, Noura Eltabakh, Mohamed Y. Rundensteiner, Elke A. Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9781450367356

The massive amounts of data series data continuously generated and collected by applications require new indices to speed up data series similarity queries on which various data mining techniques rely. However, the state-of-the-art iSAX-based indexing techniques do not scale well due to the binary fanout that leads to a highly deep index tree and suffer from accuracy degradation due to the character-level cardinality that leads to poor maintenance of the proximity. To address this problem, we recently proposed TARDIS to supports indexing and querying billion-scale data series datasets. It introduces a new iSAX-T signatures to reduce the cardinality conversion cost and corresponding sigTree to construct a compact index structure to preserve better similarity. The framework consists of one centralized index and local distributed indices to efficiently re-partition and index dimensional datasets. Besides, effective query strategies based on sigTree structure are proposed to greatly improve the accuracy. In this demonstration, we present GENET, a new interactive exploration demonstration that allows users to support Big Data Series Approximate Retrieval and Recursive Interactive Clustering in large-scale geospatial datasets using TARDIS index techniques.

关键词： Data Series distributed indexing kNN Approximate Query Clustering iSAX-T sigTree TARDIS GENET Geospatial

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Scalable distributed indexing and query processing over Linked Data

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JOURNAL OF WEB SEMANTICS 2012年 10卷 3-32页

作者： Karnstedt, Marcel Sattler, Kai-Uwe Hauswirth, Manfred Natl Univ Ireland DERI Galway Ireland Ilmenau Univ Technol Fac Comp Sci & Automat Ilmenau Germany

Linked Data is becoming the core part of modern Web applications and thus efficient access to structured information expressed in RDF gains paramount importance. A number of efficient local RDF stores exist already, while distributed indexing and distributed query processing over Linked Data with similar efficiency and data management features as known from traditional database and data integration systems are only starting to develop. distributed approaches will necessarily co-exist with centralized schemes, as data will be owned by different stakeholders who may not want to provide their complete data sets to a central place. Additionally, central/integrated storage may be prohibited for organizational or legal reasons in certain areas. To support decentralized schemes, only a few attempts in this direction exist so far, but they are limited in terms of capabilities and the degree of distribution vs. efficiency, query expressivity, and scalability. To remedy this situation, the approach and proof-of-concept prototype presented in this paper provides a solution for these open challenges. As we argue for widely distributed systems as a possible answer to scalability issues, we first identify and discuss the main challenges and based on this analysis, we propose an approach for efficient and scalable query processing over distributed Linked Data sources, taking into account the latest advances in database technology. Our system is based on a layered architecture that makes use of the advantages of decentralized indexing and query processing approaches, which have been researched and matured over the last decade. Our approach is based on a logical algebra for queries over RDF data and a related physical query algebra to enable optimization, both on the logical and physical layers in query processing. The introduced operators and strategies for processing complex query plans make excessive use of parallelism and other optimization paradigms of distributed query process

关键词： Linked Data Query processing distributed storage distributed indexing Structured queries Decentralized data management

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