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Cong Liu - IEEE Xplore Author Profile

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Author details

Cong Liu

Publications

90

Citations

1,656

Publications by Year

20082024

Co-Authors:

Vipin A.James H. AndersonJames H. AndersonyJie BaoSanjeev Baskiyar

Show All Co-Authors (142)

Cong Liu

Affiliation

University of California, Riverside

Publication Topics

Deep Neural Network,
Adversarial Attacks,
Adversarial Examples,
Target Model,
Task Scheduling,
Adversarial Perturbations,
Memory Usage,
Real-time Scheduling,
Response Time,
White-box Attack,
AUC Score,
Adversary Model

Biography

Cong Liu (Member, IEEE) received the Ph.D. degree in computer science from The University of North Carolina at Chapel Hill, in 2013. He is currently an Associate Professor with the Department of Computer Science, University of Texas at Dallas. His research interests include real-time systems and GPU. He has more than 30 published papers in premier conferences and journals. He received the Best Paper Award at the 30th IEEE RTSS and the 17th RTCSA.(Based on document published on 23 July 2020).

Publications

90

Citations

1,656

Publications by Year

20082024

Co-Authors:

Vipin A.
James H. Anderson
James H. Andersony
Jie Bao
Sanjeev Baskiyar

Show All Co-Authors (142)

Author's Published Works

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Showing 1-25 of 11,870 resultsfor "Index Terms":Intermediate Data

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Intermediate Data Format for the Elastic Data Conversion Framework

Tran Khanh Dang;Ta Manh Huy;Nguyen Le Hoang

2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)

Year: 2021 | Conference Paper |

Cited by: Papers (5)

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With the development of technology, data is becoming an extremely valuable resource. Data is being created, analyzed and used in a massive scale in every modern system. As a result, data analysis and data mining are very essential in each aspect of social applications. The value of data will be more useful if it can be linked and merged with other different data resources, especially for solving c...Show More

Intermediate Data Format for the Elastic Data Conversion Framework

Tran Khanh Dang;Ta Manh Huy;Nguyen Le Hoang

2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)

Year: 2021 | Conference Paper |

Data Mining Technology and Its Application in Multi-sensor Data Processing

Junjun Xu;Yunhui Guo;Jiang Wang

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)

Year: 2022 | Conference Paper |

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Big data contains huge data value, can effectively promote the development of university information; However, there are many difficulties in the application of big data in multi-sensor data processing. In view of the practical problems faced by the current multi-sensor data processing and big data applications, the overall architecture design and the construction of core modules of multi-sensor d...Show More

Data Mining Technology and Its Application in Multi-sensor Data Processing

Junjun Xu;Yunhui Guo;Jiang Wang

2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA)

Year: 2022 | Conference Paper |

Incremental MapReduce for K-Medoids Clustering of Big Time-Series Data

Divyaprabha Rajendran;Saikishor Jangiti;Sowmiya Muralidharan;M Thendral

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)

Year: 2018 | Conference Paper |

Cited by: Papers (1)

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There is a high necessity to refresh the data mining results, as the former results become stale and obsolete over time due to dynamic and evolving data. Clustering is one of the important data mining techniques that help to group data points with similarity together. To mine the data generated exponentially in these days, MapReduce, a parallel programming framework can be combined MapReduce with ...Show More

Incremental MapReduce for K-Medoids Clustering of Big Time-Series Data

Divyaprabha Rajendran;Saikishor Jangiti;Sowmiya Muralidharan;M Thendral

2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI)

Year: 2018 | Conference Paper |

Reducing the Search Space for Big Data Mining for Interesting Patterns from Uncertain Data

Carson Kai-Sang Leung;Richard Kyle MacKinnon;Fan Jiang

2014 IEEE International Congress on Big Data

Year: 2014 | Conference Paper |

Cited by: Papers (45) | Patents (3)

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Many existing data mining algorithms search interesting patterns from transactional databases of precise data. However, there are situations in which data are uncertain. Items in each transaction of these probabilistic databases of uncertain data are usually associated with existential probabilities, which express the likelihood of these items to be present in the transaction. When compared with m...Show More

Reducing the Search Space for Big Data Mining for Interesting Patterns from Uncertain Data

Carson Kai-Sang Leung;Richard Kyle MacKinnon;Fan Jiang

2014 IEEE International Congress on Big Data

Year: 2014 | Conference Paper |

Hybrid big data warehouse for on-demand decision needs

Meryeme El Houari;Maryem Rhanoui;Bouchra El Asri

2017 International Conference on Electrical and Information Technologies (ICEIT)

Year: 2017 | Conference Paper |

Cited by: Papers (3)

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Every day trillions of data are generated across the world and put the information systems facing the emergence of big data phenomenon. This vertiginous evolution makes the enterprise confronting the challenge to build its own big data. To achieve the challenge, the enterprise is supposed to embark on big investments in terms of resource and material to process petabytes of diverse data, this last...Show More

Hybrid big data warehouse for on-demand decision needs

Meryeme El Houari;Maryem Rhanoui;Bouchra El Asri

2017 International Conference on Electrical and Information Technologies (ICEIT)

Year: 2017 | Conference Paper |

Queries over Large-Scale Incremental Data of Hybrid Granularities

Xutian Zhuang;Gansen Zhao;Xinming Wang;Ruihua Nie;Zhirui Liao;Chengchuang Lin;Zhenyu Li

2016 7th International Conference on Cloud Computing and Big Data (CCBD)

Year: 2016 | Conference Paper |

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The development of Internet and Web systems in recent years has made it difficult and challenging to deal with large-scale data. What we usually need to process is incremental data, the scale of which will enlarge with the changing of time. Nowadays, many general queries over data is traditionally based on the full amount of raw data, which becomes a great challenge to performance when the data in...Show More

Queries over Large-Scale Incremental Data of Hybrid Granularities

Xutian Zhuang;Gansen Zhao;Xinming Wang;Ruihua Nie;Zhirui Liao;Chengchuang Lin;Zhenyu Li

2016 7th International Conference on Cloud Computing and Big Data (CCBD)

Year: 2016 | Conference Paper |

A Data Science Solution for Mining Interesting Patterns from Uncertain Big Data

Carson Kai-Sang Leung;Fan Jiang

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

Year: 2014 | Conference Paper |

Cited by: Papers (51)

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Nowadays, high volumes of valuable uncertain data can be easily collected or generated at high velocity in many real-life applications. Mining these uncertain Big data is computationally intensive due to the presence of existential probability values associated with items in every transaction in the uncertain data. Each existential probability value expresses the likelihood of that item to be pres...Show More

A Data Science Solution for Mining Interesting Patterns from Uncertain Big Data

Carson Kai-Sang Leung;Fan Jiang

2014 IEEE Fourth International Conference on Big Data and Cloud Computing

Year: 2014 | Conference Paper |

Cost-Efficient Big Intermediate Data Placement in a Collaborative Cloud Storage Environment

Sonia Ikken;Éric Renault;Amine Barkat;Abdelkamel Tari;Tahar Kechad

2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

Year: 2017 | Conference Paper |

Cited by: Papers (3)

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Collaborative cloud storage environment, which share resources of multiple geographically distributed datacenters owned by different providers enable scientific workflow from different locations to process large scale big intermediate data through the Internet. Distributed datacenters are federated and each member can collaborate with each other to efficiently share and process the intermediate da...Show More

Cost-Efficient Big Intermediate Data Placement in a Collaborative Cloud Storage Environment

Sonia Ikken;Éric Renault;Amine Barkat;Abdelkamel Tari;Tahar Kechad

2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

Year: 2017 | Conference Paper |

Reusing Garbage Data for Efficient Workflow Computation

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The Computer Journal

Year: 2015 | Volume: 58, Issue: 1 | Journal Article |

Cited by: Papers (2)

High-performance computing (HPC) systems, including Clusters, Grids and the most recent Clouds, have emerged as attractive platforms to tackle various applications. One significant type of applications in the HPC systems is workflow computation, which has been applied in various scientific and engineering domains. The workflow computation frequently produces intermediate result files, which become...Show More

Reusing Garbage Data for Efficient Workflow Computation

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The Computer Journal

Year: 2015 | Volume: 58, Issue: 1 | Journal Article |

BSDP: A Novel Balanced Spark Data Partitioner

Aibo Song;Bowen Peng;Jingyi Qiu;Yingying Xue;Mingyang Du

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2021 | Conference Paper |

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As a memory-based distributed big data computing framework, Spark has been widely used in big data processing systems. However, during the execution of Spark, due to the imbalance of input data distribution and the shortage of existing data partitioners in Spark, it is easy to cause partition skew problem and reduce the execution efficiency of Spark. Aiming at this problem, this paper proposes a b...Show More

BSDP: A Novel Balanced Spark Data Partitioner

Aibo Song;Bowen Peng;Jingyi Qiu;Yingying Xue;Mingyang Du

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)

Year: 2021 | Conference Paper |

Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design

Linlin You;Bige Tunçer;Hexu Xing

IEEE Transactions on Knowledge and Data Engineering

Year: 2019 | Volume: 31, Issue: 2 | Journal Article |

Cited by: Papers (14)

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The intelligence of Smart Cities (SC) is represented by its ability in collecting, managing, integrating, analyzing, and mining multi-source data for valuable insights. In order to harness multi-source data for an informed place design, this paper presents “Public Sentiments and Activities in Places” multi-source data analysis flow (PSAP) in an Informed Design Platform (IDP). In terms of key contr...Show More

Harnessing Multi-Source Data about Public Sentiments and Activities for Informed Design

Linlin You;Bige Tunçer;Hexu Xing

IEEE Transactions on Knowledge and Data Engineering

Year: 2019 | Volume: 31, Issue: 2 | Journal Article |

A Fast, Scalable, Universal Approach For Distributed Data Aggregations

Niranda Perera;Vibhatha Abeykoon;Chathura Widanage;Supun Kamburugamuve;Thejaka Amila Kanewala;Pulasthi Wickramasinghe;Ahmet Uyar;Hasara Maithree;Damitha Lenadora;Geoffrey Fox

2020 IEEE International Conference on Big Data (Big Data)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new applications in both enterprise and research communities. Aggregations (also termed reduce in functional programming) are an integral functionality in these appl...Show More

A Fast, Scalable, Universal Approach For Distributed Data Aggregations

Niranda Perera;Vibhatha Abeykoon;Chathura Widanage;Supun Kamburugamuve;Thejaka Amila Kanewala;Pulasthi Wickramasinghe;Ahmet Uyar;Hasara Maithree;Damitha Lenadora;Geoffrey Fox

2020 IEEE International Conference on Big Data (Big Data)

Year: 2020 | Conference Paper |

Applied research on visual mining technology in medical data

Chunshan Wu;Yongqiang Weng;Qiaowei Jiang;Wenming Guo;Cong Wang

2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS)

Year: 2016 | Conference Paper |

Cited by: Papers (4)

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Data mining is a process of discovering information from large datasets by using automated algorithms, which makes it difficult for users to intuitively understand, explore and optimize the datasets and algorithms process. Recently with the development of the visual field, researchers have been exploring how to use the visualization methods to make users intuitively understand datasets and algorit...Show More

Applied research on visual mining technology in medical data

Chunshan Wu;Yongqiang Weng;Qiaowei Jiang;Wenming Guo;Cong Wang

2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS)

Year: 2016 | Conference Paper |

A study of the time effect during data processing

Third International Conference on Information Technology and Applications (ICITA'05)

Year: 2005 | Conference Paper |

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The study of the time effect of data plays a very important role in the real world of data processing. With the ever increasing data accumulation, it is vital to determine a time frame in which the collected data remains meaningful and valid. Without such study and action, much of the vast data source will create confusion, which can result in the data totally unusable. This paper offers a compreh...Show More

A study of the time effect during data processing

Third International Conference on Information Technology and Applications (ICITA'05)

Year: 2005 | Volume: 1 | Conference Paper |

Implementation of on-process aggregation for efficient big data processing in Hadoop MapReduce environment

Vidya V. Pol;S.M. Patil

2016 International Conference on Inventive Computation Technologies (ICICT)

Year: 2016 | Conference Paper |

Cited by: Papers (4)

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The term Big Data, refers to sizably voluminous data whose volume, variability, and velocity make it very arduous to manage, process or analyzed. To analyze this sizably voluminous kind of data Hadoop will be utilized. However, Processing is very time-consuming. To resolve this quandary & to decrement replication time one solution is to executing the job partially, where an approximate, early resu...Show More

Implementation of on-process aggregation for efficient big data processing in Hadoop MapReduce environment

Vidya V. Pol;S.M. Patil

2016 International Conference on Inventive Computation Technologies (ICICT)

Year: 2016 | Volume: 3 | Conference Paper |

HIDStore: A Hierarchical Intermediate Data Storage System for Seismic Processing Application

Yida Wang;Changhai Zhao;Zengbo Wang;Chao Liu;Chao Li;Haihua Yan;Jiamin Wen

2018 IEEE International Conference on Cluster Computing (CLUSTER)

Year: 2018 | Conference Paper |

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Seismic processing is an important technology in petroleum industry. During the execution of seismic processing applications, large amount of intermediate data are generated and accessed. Providing high-performance services for intermediate data in the traditional storage architecture is expensive. In addition, because of the existence of new storage devices, the heterogeneity of storage environme...Show More

HIDStore: A Hierarchical Intermediate Data Storage System for Seismic Processing Application

Yida Wang;Changhai Zhao;Zengbo Wang;Chao Liu;Chao Li;Haihua Yan;Jiamin Wen

2018 IEEE International Conference on Cluster Computing (CLUSTER)

Year: 2018 | Conference Paper |

A New Approach to Secure Aggregation of Private Data in Wireless Sensor Networks

Rabindra Bista;Kyoung-Jin Jo;Jae-Woo Chang

2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing

Year: 2009 | Conference Paper |

Cited by: Papers (10) | Patents (2)

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Many applications require the privacy of the sampled data while they travel from the source sensor nodes to data collecting device, say data sink. Providing an efficient data aggregation scheme with preserving data privacy is a challenging problem in the research of wireless sensor networks (WSNs). Although the secure data aggregation in WSNs has been well studied in the recent years, there exists...Show More

A New Approach to Secure Aggregation of Private Data in Wireless Sensor Networks

Rabindra Bista;Kyoung-Jin Jo;Jae-Woo Chang

2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing

Year: 2009 | Conference Paper |

Efficiency Improvements in Social Network Communication via MapReduce

Fan Jiang;Carson K. Leung;Dacheng Liu

2015 IEEE International Conference on Data Science and Data Intensive Systems

Year: 2015 | Conference Paper |

Cited by: Papers (5)

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As we are living in a "smart world" (which comprises cyber, physical and social worlds), big data are everywhere. High volumes of high-veracious, high-valuable data can be easily generated and collected at a high velocity from a high variety of data sources in various real-life applications in the fields of sciences and engineering, finance, social media, as well as online information resources. T...Show More

Efficiency Improvements in Social Network Communication via MapReduce

Fan Jiang;Carson K. Leung;Dacheng Liu

2015 IEEE International Conference on Data Science and Data Intensive Systems

Year: 2015 | Conference Paper |

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

Qiufen Xia;Zichuan Xu;Weifa Liang;Albert Y. Zomaya

IEEE Transactions on Parallel and Distributed Systems

Year: 2016 | Volume: 27, Issue: 7 | Journal Article |

Cited by: Papers (35)

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With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great...Show More

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

Qiufen Xia;Zichuan Xu;Weifa Liang;Albert Y. Zomaya

IEEE Transactions on Parallel and Distributed Systems

Year: 2016 | Volume: 27, Issue: 7 | Journal Article |

Real Time Biomedical Data Streaming Platform (RIMES): A Data-Intensive Virtual Environment

Kejun Dong;Kai Nan;Sameer Tilak;Cindy Zheng;Dong Xu;Jurgen Schulze;Peter Arzberger;Wilfred Li

2010 Third International Joint Conference on Computational Science and Optimization

Year: 2010 | Conference Paper |

Cited by: Papers (3)

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In this paper we present Real time biomedical data Streaming platform (RIMES), a data-intensive virtual environment, under joint development by Computer Network Information Center and University of California, San Diego. RIMES extends scientists' desktops to biomedical grid computation resources and mass storage systems, by means of streaming biomedical data through internet-based Data Turbine clo...Show More

Real Time Biomedical Data Streaming Platform (RIMES): A Data-Intensive Virtual Environment

Kejun Dong;Kai Nan;Sameer Tilak;Cindy Zheng;Dong Xu;Jurgen Schulze;Peter Arzberger;Wilfred Li

2010 Third International Joint Conference on Computational Science and Optimization

Year: 2010 | Volume: 2 | Conference Paper |

Analysis of Multiagent System for Data Analysis

Yuleisy González Pérez;Ivan I. Kholod

2020 XXIII International Conference on Soft Computing and Measurements (SCM)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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Agents are an important part of solving many current problems in distributed computing, including complexity and information overload. There is a need to integrate multiagent technology and data mining to counter the growth of heterogeneous and distributed data sources. This article describes the main concepts: agents, data mining, and analyzes the possibilities of their integration. A comparative...Show More

Analysis of Multiagent System for Data Analysis

Yuleisy González Pérez;Ivan I. Kholod

2020 XXIII International Conference on Soft Computing and Measurements (SCM)

Year: 2020 | Conference Paper |

Storage cost efficient approach for cloud storage and intermediate data sets in clouds

Mayank Deep Khare;Aditi Tripathi;Pradeep Kumar Singh

2015 International Conference on Green Computing and Internet of Things (ICGCIoT)

Year: 2015 | Conference Paper |

Cited by: Papers (3)

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Now-a-days we are leading a life with world wide access technologies. Cloud computing is one of such technologies via which we can ensure security, disaster recovery, and better availability of data. Cloud computing basically focus on deployment of remote servers and software network together. Cloud computing allows us online access of the resource remotely. It benefits us while we use it for dist...Show More

Storage cost efficient approach for cloud storage and intermediate data sets in clouds

Mayank Deep Khare;Aditi Tripathi;Pradeep Kumar Singh

2015 International Conference on Green Computing and Internet of Things (ICGCIoT)

Year: 2015 | Conference Paper |

Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data

Gang Luo;Lewis J. Frey

IEEE Journal of Biomedical and Health Informatics

Year: 2016 | Volume: 20, Issue: 2 | Journal Article |

Cited by: Papers (8)

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Entity-attribute-value (EAV) tables are widely used to store data in electronic medical records and clinical study data management systems. Before they can be used by various analytical (e.g., data mining and machine learning) programs, EAV-modeled data usually must be transformed into conventional relational table format through pivot operations. This time-consuming and resource-intensive process...Show More

Efficient Execution Methods of Pivoting for Bulk Extraction of Entity-Attribute-Value-Modeled Data

Gang Luo;Lewis J. Frey

IEEE Journal of Biomedical and Health Informatics

Year: 2016 | Volume: 20, Issue: 2 | Journal Article |

Real time interpretation and optimization of time series data stream in big data

Zheyuan Jiang;Ke Liu

2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

Year: 2018 | Conference Paper |

Cited by: Papers (3)

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In view of massive historical data and high-speed time series data stream in the futures market program trading, how to mine related data and explain data real-timely, this paper proposes a real-timely proceeding model for massive data based on ARTMMR algorithm. The ARTMMR algorithm obtains frequent itemsets by using the parallelism of MapReduce, which saves the storage space and decreases the tim...Show More

Real time interpretation and optimization of time series data stream in big data

Zheyuan Jiang;Ke Liu

2018 IEEE 3rd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

Year: 2018 | Conference Paper |

Second-Generation Big Data Systems

Fadi H. Gebara;H. Peter Hofstee;Kevin J. Nowka

Year: 2015 | Volume: 48, Issue: 1 | Magazine Article |

Cited by: Papers (13)

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More varied data channels, increasingly diverse analytic methods, and new deployment models--along with some fundamental technology shifts--will significantly impact the next generation of big data systems.Show More

Second-Generation Big Data Systems

Fadi H. Gebara;H. Peter Hofstee;Kevin J. Nowka

Year: 2015 | Volume: 48, Issue: 1 | Magazine Article |

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