NSTL国家科技图书文献中心

1. Global reduction for geo-distributed MapReduce across cloud federation NSTL国家科技图书文献中心

Thouraya Gouasmi | Ahmed Hadj Kacem - 《Future generations computer systems》 - 2025,162(Jan.) - 107492.1～107492.12 - 共12页

摘要： MapReduce-based framework across federated cloud based on | , we propose an exact MapReduce* scheduling model for* | Geo-distributed Bigdata processing is | increasing day by day, resulting in the origins of data | that are geographically distributed in different

关键词： MapReduce | Cloud federation | BigData | Geo-distributed scheduling | Cost optimization

2. STORM: A MapReduce Framework for Symbolic Time Intervals Series Classification NSTL国家科技图书文献中心

OMER DAVID HAREL | ROBERT MOSKOVITCH - 《ACM transactions on knowledge discovery from data》 - 2025,19(1) - 3.1～3.54 - 共54页

摘要： introduce STORMa novel, generic MapReduce* framework for* | Symbolic Time Intervals (STIs) represent | events having a non-zero time duration, which are | common in various application domains. In this article | , we focus on the challenge of STIs series

关键词： Symbolic Time Intervals | Classification | MapReduce | Rocket | LSTM

3. Rapid and optimized parallel attribute reduction based on neighborhood rough sets and MapReduce NSTL国家科技图书文献中心

V. K. Hanuman Turaga | Srilatha Chebrolu - 《Expert Systems with Application》 - 2025,260(Jan.) - 125323.1～125323.18 - 共18页

摘要：Attribute reduction is a crucial step in data | pre-processing and feature engineering. It is the | selection of a subset of relevant data attributes to | reduce the computational complexity of machine learning | models and improve their performance. Neighborhood

关键词： Attribute reduction | Neighborhood rough sets | MapReduce | Neighborhood information | Data preprocessing | Computational complexity | High-dimensional data

4. Hybrid jellyfish search sine cosine optimisation-based deep learning for big data classification using MapReduce framework on epileptic seizure data NSTL国家科技图书文献中心

Jamunadevi Chandrase... | Arul Ponnusamy - 《International journal of intelligent information and database systems》 - 2025,17(1) - 1～31 - 共31页

摘要： MapReduce framework on epileptic seizure data is proposed | a MapReduce* framework, wherein the mapper phase is* | The increase in the amount of big data with | the technical advances makes the traditional | software tools face difficulties and unable to handle

关键词： deep embedded clustering | DEC | jellyfish search sine cosine algorithm | JSCS | deep long short-term memory | big data classification | epileptic seizure detection

5. Optimizing Geo-Distributed Data Processing with Resource Heterogeneity over the Internet NSTL国家科技图书文献中心

SAEED MIRPOUR MARZUN... | ADEL TOOSI ... - 《ACM Transactions on Internet Technology》 - 2025,25(1) - 5～ - 共28页

摘要：The traditional MapReduce* frameworks were* | MapReduce operations adds unnecessary complexity. To | -MapReduce* (ECMR), a framework that integrates resource* | heterogeneity and network links in geo-distributed MapReduce | originally designed for processing data within a single

关键词： MapReduce | Geo-distributed data | Data centers | Big data | Multi-cluster

6. TEE-MR: Developer-friendly data oblivious programming for trusted execution environments NSTL国家科技图书文献中心

A.K.M. Mubashwir Ala... | Keke Chen - 《Computers & Security》 - 2025,148(Jan.) - 104119.1～104119.15 - 共15页

摘要： oblivious TEE with MapReduce* (TEE-MR) approach that uses* | . We have implemented the approach with the MapReduce | Trusted execution environments (TEEs) enable | efficient protection of integrity and confidentiality for | applications running on untrusted platforms. They have been

关键词： TEE | SGX | MapReduce | Data analytics | Dataflow | Access patterns | ORAM

7. HEDAS: Secure and Efficient Distributed OLAP Using Fully Homomorphic Encryption NSTL国家科技图书文献中心

Yu Tian | Tianxiang Shen ... - 《Computer Security,Part I》 - European Symposium on Research in Computer Security | International Workshop on Data Privacy Management | International Workshop on Cryptocurrencies and Blockchain Technology | International Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems - 2025, - 77～93 - 共17页

摘要： machines, inspired by the MapReduce* model, to reduce end* | leveraging the MapReduce* model, Hedas effectively* | The popularity of cloud computing has | revolutionized Online Analytical Processing (OLAP), yet risks | of privacy leakage limit the use of public clouds

关键词： Fully homomorphic encryption (FHE) | Online analytical processing (OLAP) | MapReduce

8. Modelling of healthcare data analytics using optimal machine learning model in big data environment NSTL国家科技图书文献中心

Chelladurai Fancy | Nagappan Krishnaraj ... - 《Expert systems》 - 2025,42(1) - e13612.1～e13612.15 - 共15页

摘要： uses Hadoop MapReduce* environment. In addition, the* | Recent advances in wireless networking, big | data technologies, namely Internet of Things (IoT) 5G | networks, health care big data analytics, and other | technologies in artificial intelligence (AI) and wearables

关键词： big data | disease diagnosis | feature selection | Hadoop MapReduce | healthcare | machine learning

9. Intelligent decision-making framework for big data using enhanced honey badger-based adaptive hybrid deep learning network NSTL国家科技图书文献中心

Kavitha, D. | Chinnasamy, A.... - 《International journal of data mining and bioinformatics》 - 2025,29(1/2) - 193～222 - 共31页

摘要：By utilising the conventional models, it is | also consuming more time for processing. Hence, there | is a crucial requirement for real-world application | over big data procedures to perform a scalable and | effective solution. For the experimentation, input data is

关键词： decision making | big data | enhanced honey badger algorithm | adaptive cascaded long short-term memory | auto-encoder | MapReduce framework

10. Stateful MapReduce Framework for mRMR Feature Selection Using Horizontal Partitioning NSTL国家科技图书文献中心

Vivek Yelleti | P. S. V. S. Sai Pras... - 《Pattern Recognition and Machine Intelligence: 9th International Conference, PReMI 2021, Kolkata, India, December 15-18, 2021, Proceedings》 - International Conference on Pattern Recognition and Machine Intelligence - 2024, - 317～327 - 共11页

摘要： distributed/parallel algorithms. MapReduce* solutions are* | existing Horizontal MapReduce* approaches for mRMR feature* | partitioning based MapReduce* solutions namely HMR_mRMR, is an* | Iterative MapReduce* algorithms and is designed under* | Feature selection (FS) is an important pre

摘要： MapReduce-based framework across federated cloud based on | , we propose an exact MapReduce* scheduling model for* | Geo-distributed Bigdata processing is | increasing day by day, resulting in the origins of data | that are geographically distributed in different

关键词： MapReduce | Cloud federation | BigData | Geo-distributed scheduling | Cost optimization

摘要： introduce STORMa novel, generic MapReduce* framework for* | Symbolic Time Intervals (STIs) represent | events having a non-zero time duration, which are | common in various application domains. In this article | , we focus on the challenge of STIs series

关键词： Symbolic Time Intervals | Classification | MapReduce | Rocket | LSTM

摘要：Attribute reduction is a crucial step in data | pre-processing and feature engineering. It is the | selection of a subset of relevant data attributes to | reduce the computational complexity of machine learning | models and improve their performance. Neighborhood

关键词： Attribute reduction | Neighborhood rough sets | MapReduce | Neighborhood information | Data preprocessing | Computational complexity | High-dimensional data

摘要： MapReduce framework on epileptic seizure data is proposed | a MapReduce* framework, wherein the mapper phase is* | The increase in the amount of big data with | the technical advances makes the traditional | software tools face difficulties and unable to handle

关键词： deep embedded clustering | DEC | jellyfish search sine cosine algorithm | JSCS | deep long short-term memory | big data classification | epileptic seizure detection

摘要：The traditional MapReduce* frameworks were* | MapReduce operations adds unnecessary complexity. To | -MapReduce* (ECMR), a framework that integrates resource* | heterogeneity and network links in geo-distributed MapReduce | originally designed for processing data within a single

关键词： MapReduce | Geo-distributed data | Data centers | Big data | Multi-cluster

摘要： oblivious TEE with MapReduce* (TEE-MR) approach that uses* | . We have implemented the approach with the MapReduce | Trusted execution environments (TEEs) enable | efficient protection of integrity and confidentiality for | applications running on untrusted platforms. They have been

关键词： TEE | SGX | MapReduce | Data analytics | Dataflow | Access patterns | ORAM

摘要： machines, inspired by the MapReduce* model, to reduce end* | leveraging the MapReduce* model, Hedas effectively* | The popularity of cloud computing has | revolutionized Online Analytical Processing (OLAP), yet risks | of privacy leakage limit the use of public clouds

关键词： Fully homomorphic encryption (FHE) | Online analytical processing (OLAP) | MapReduce

摘要： uses Hadoop MapReduce* environment. In addition, the* | Recent advances in wireless networking, big | data technologies, namely Internet of Things (IoT) 5G | networks, health care big data analytics, and other | technologies in artificial intelligence (AI) and wearables

关键词： big data | disease diagnosis | feature selection | Hadoop MapReduce | healthcare | machine learning

摘要：By utilising the conventional models, it is | also consuming more time for processing. Hence, there | is a crucial requirement for real-world application | over big data procedures to perform a scalable and | effective solution. For the experimentation, input data is

关键词： decision making | big data | enhanced honey badger algorithm | adaptive cascaded long short-term memory | auto-encoder | MapReduce framework

摘要： distributed/parallel algorithms. MapReduce* solutions are* | existing Horizontal MapReduce* approaches for mRMR feature* | partitioning based MapReduce* solutions namely HMR_mRMR, is an* | Iterative MapReduce* algorithms and is designed under* | Feature selection (FS) is an important pre

关键词： Feature selection | MRMR | Big data | Horizontal partitioning | MapReduce | Iterative mapReduce

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： MapReduce-based framework across federated cloud based on | , we propose an exact MapReduce scheduling model for | Geo-distributed Bigdata processing is | increasing day by day, resulting in the origins of data | that are geographically distributed in different

关键词： MapReduce | Cloud federation | BigData | Geo-distributed scheduling | Cost optimization

摘要： introduce STORMa novel, generic MapReduce framework for | Symbolic Time Intervals (STIs) represent | events having a non-zero time duration, which are | common in various application domains. In this article | , we focus on the challenge of STIs series

关键词： Symbolic Time Intervals | Classification | MapReduce | Rocket | LSTM

摘要：Attribute reduction is a crucial step in data | pre-processing and feature engineering. It is the | selection of a subset of relevant data attributes to | reduce the computational complexity of machine learning | models and improve their performance. Neighborhood

关键词： Attribute reduction | Neighborhood rough sets | MapReduce | Neighborhood information | Data preprocessing | Computational complexity | High-dimensional data

摘要： MapReduce framework on epileptic seizure data is proposed | a MapReduce framework, wherein the mapper phase is | The increase in the amount of big data with | the technical advances makes the traditional | software tools face difficulties and unable to handle

关键词： deep embedded clustering | DEC | jellyfish search sine cosine algorithm | JSCS | deep long short-term memory | big data classification | epileptic seizure detection

摘要：The traditional MapReduce frameworks were | MapReduce operations adds unnecessary complexity. To | -MapReduce (ECMR), a framework that integrates resource | heterogeneity and network links in geo-distributed MapReduce | originally designed for processing data within a single

关键词： MapReduce | Geo-distributed data | Data centers | Big data | Multi-cluster

摘要： oblivious TEE with MapReduce (TEE-MR) approach that uses | . We have implemented the approach with the MapReduce | Trusted execution environments (TEEs) enable | efficient protection of integrity and confidentiality for | applications running on untrusted platforms. They have been

关键词： TEE | SGX | MapReduce | Data analytics | Dataflow | Access patterns | ORAM

摘要： machines, inspired by the MapReduce model, to reduce end | leveraging the MapReduce model, Hedas effectively | The popularity of cloud computing has | revolutionized Online Analytical Processing (OLAP), yet risks | of privacy leakage limit the use of public clouds

关键词： Fully homomorphic encryption (FHE) | Online analytical processing (OLAP) | MapReduce

摘要： uses Hadoop MapReduce environment. In addition, the | Recent advances in wireless networking, big | data technologies, namely Internet of Things (IoT) 5G | networks, health care big data analytics, and other | technologies in artificial intelligence (AI) and wearables

关键词： big data | disease diagnosis | feature selection | Hadoop MapReduce | healthcare | machine learning

摘要：By utilising the conventional models, it is | also consuming more time for processing. Hence, there | is a crucial requirement for real-world application | over big data procedures to perform a scalable and | effective solution. For the experimentation, input data is

关键词： decision making | big data | enhanced honey badger algorithm | adaptive cascaded long short-term memory | auto-encoder | MapReduce framework

摘要： distributed/parallel algorithms. MapReduce solutions are | existing Horizontal MapReduce approaches for mRMR feature | partitioning based MapReduce solutions namely HMR_mRMR, is an | Iterative MapReduce algorithms and is designed under | Feature selection (FS) is an important pre

关键词： Feature selection | MRMR | Big data | Horizontal partitioning | MapReduce | Iterative mapReduce

4008-161-200 800-990-8900

国家科技图书文献中心

摘要： MapReduce-based framework across federated cloud based on | , we propose an exact MapReduce* scheduling model for* | Geo-distributed Bigdata processing is | increasing day by day, resulting in the origins of data | that are geographically distributed in different

摘要： introduce STORMa novel, generic MapReduce* framework for* | Symbolic Time Intervals (STIs) represent | events having a non-zero time duration, which are | common in various application domains. In this article | , we focus on the challenge of STIs series

摘要： MapReduce framework on epileptic seizure data is proposed | a MapReduce* framework, wherein the mapper phase is* | The increase in the amount of big data with | the technical advances makes the traditional | software tools face difficulties and unable to handle

摘要：The traditional MapReduce* frameworks were* | MapReduce operations adds unnecessary complexity. To | -MapReduce* (ECMR), a framework that integrates resource* | heterogeneity and network links in geo-distributed MapReduce | originally designed for processing data within a single

摘要： oblivious TEE with MapReduce* (TEE-MR) approach that uses* | . We have implemented the approach with the MapReduce | Trusted execution environments (TEEs) enable | efficient protection of integrity and confidentiality for | applications running on untrusted platforms. They have been

摘要： machines, inspired by the MapReduce* model, to reduce end* | leveraging the MapReduce* model, Hedas effectively* | The popularity of cloud computing has | revolutionized Online Analytical Processing (OLAP), yet risks | of privacy leakage limit the use of public clouds

摘要： uses Hadoop MapReduce* environment. In addition, the* | Recent advances in wireless networking, big | data technologies, namely Internet of Things (IoT) 5G | networks, health care big data analytics, and other | technologies in artificial intelligence (AI) and wearables

摘要： distributed/parallel algorithms. MapReduce* solutions are* | existing Horizontal MapReduce* approaches for mRMR feature* | partitioning based MapReduce* solutions namely HMR_mRMR, is an* | Iterative MapReduce* algorithms and is designed under* | Feature selection (FS) is an important pre