Pinchao Liu’s Paper Is Accepted At IEEE IPDPS 2020

Congratulation to Pinchao! His following paper is accepted at IEEE IPDPS 2020.

  • Pinchao Liu, Liting Hu, Hailu Xu, Dilma Da Silva, Qingyang Wang and Sarker Tanzir Ahmed. “FP4S: Fragment-based Parallel State Recovery for Stateful Stream Applications”, In Proceedings of 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020), May 2020.
  • Hailu Xu Won Best Student Paper In Conference SCF2019

    Congratulations to Hailu Xu! His paper “Exploiting the Spam Correlations in Scalable Online Social Spam Detection” won the SCF2019 best student paper awards.

    Boyuan Guan Participate IEEE Big Data Congress 2019 at Milan

    Boyuan presented ELVES lab work “dpSmart: a Flexible Group based Recommendation Framework for Digital Repository Systems” in the conference IEEE Big Data Congress 2019 at Milan, Italy.

    The presentation can be found at here.

    The paper’s abstract is as below:

    Digital Repository Systems have been used in most modern digital library platforms. Even so, Digital Repository Systems often suffer from problems such as low discoverability, poor usability, and high drop-off visit rates. With these problems, the majority of the content in the digital library platforms may not be exposed to end users, while at the same time, users are desperately looking for something which may not be returned from the platforms. The recommendation systems for digital libraries were proposed to solve these problems. However, most recommendation systems have been implemented by directly adopting one specific type of recommender like Collaborative-Filtering (CF), Content-Based Filtering (CBF), Stereotyping, or hybrid recommenders. As such, they are either (1) not able to accommodate the variation of the user groups, (2) require too much labor, or (3) require intensive computational complexity.

    In this paper, we design and implement a new recommendation system framework for Digital Repository Systems, named dpSmart, which allows multiple recommenders to work collaboratively on the same platform. In the proposed system, a user-group based recommendation strategy is applied to accommodate the requirements from the different types of users. A user recognition model is built, which can avoid the intensive labor of the stereotyping recommender. We implement the system prototype as a sub-system of the FIU library site and evaluate it on January 2019 and February 2019. During this time, the Page Views have increased from 8,502 to 10,916 and 10,942 to 12,314 respectively, compared to 2018, demonstrating the effectiveness of our proposed system.

    Pinchao Liu Participate IEEE ICDIS 2019 at South Padre Island

    Pinchao presented ELVES lab work “Towards Adaptive Replication for Hot/Cold Blocks in HDFS using MemCached” in the conference IEEE ICDIS 2019 at South Padre Island.

    The presentation can be found here.

    The paper’s abstract is as below:

    With the advancement of ever-growing online services, distributed Big Data storage i.e. Hadoop, Dryad gained much more attention than ever and the fundamental requirements like fault tolerance and data availability become the concern for these platforms. Data replication policies in Big Data applications are shifting towards dynamic approaches based on the popularity of files. Formulation of dynamic replication factor paved the way of solving the issues generated by existing data contention in hotspots and ensuring timely data availability. But from the empirical observations, it can be deduced that popularity of files is temporal rather than perpetual in nature and, after a certain period, content’s popularity ceases most of the time which introduces the I/O bottleneck of updating replication in the disk. To handle such temporal skewed popularity of contents, we propose a dynamic data replication toolset using the power of in-memory processing by integrating MemCached server into Hadoop for getting improved performance. We compare the proposed algorithm with the traditional infrastructure and vanilla memory algorithm, as the evidence from the experimental results, the proposed design performs better i.e throughput and execution period.

    Hailu Xu Participate CLOUD 2019 at San Diego

    Hailu presented ELVES lab work “Exploiting the Spam Correlations in Scalable Online Social Spam Detection” in the conference CLOUD 2019 at San Diego.

    The presentation can be found at here.

    The paper’s abstract is as below:

    The huge amount of social spam from large-scale social net-works has been a common phenomenon in the contemporary world. The majority of former research focused on improving the efficiency of identifying social spam from a limited size of data in the algorithm side,however, few of them target on the data correlations among large-scaled is tributed social spam and utilize the benefits from the system side.In this paper, we propose a new scalable system, named SpamHunter,which can utilize the spam correlations from distributed data sources toenhance the performance of large-scale social spam detection. It identifies the correlated social spam from various distributed servers/sources through DHT-based hierarchical functional trees. These functional treesact as bridges among data servers/sources to aggregate, exchange, and communicate the updated and newly emerging social spam with each other. Furthermore, by processing the online social logs instantly, it al-lows online streaming data to be processed in a distributed manner,which reduces the online detection latency and avoids the inefficiency of outdated spam posts. Our experimental results with real-world social logs demonstrate that SpamHunter reaches 95% F1 score in the spam detection, achieves high efficiency in scaling to a large amount of data servers with low latency.

    Boyuan Guan’s Paper Is Accepted At IEEE Big Data Congress 2019

    Congratulation to Boyuan! His following paper is accept at IEEE Big Data Congress 2019.

     

    • Boyuan Guan, Liting Hu, Pinchao Liu, Hailu Xu, Jennifer Fu, Qingyang Wang. “dpSmart: a Flexible Group based Recommendation Framework for Digital Repository Systems”, In Proceedings of the 2019 International Congress on Big Data (Big Data Congress), 2019.

    Hailu Xu’s Paper Is Accepted At CLOUD 2019

    Congratulation to Hailu! His following paper is accept at CLOUD 2019.

     

    • Hailu Xu, Liting Hu, Pinchao Liu, and Boyuan Guan, “Exploiting the Spam Correlations in Scalable Online Social Spam Detection”, In Proceedings of 2019 International Conference on Cloud Computing (CLOUD 2019), June 2019.

    Pinchao Liu’s Paper Is Accepted At IEEE ICDIS 2019

    Congratulation to Pinchao! His following paper is accept at IEEE ICDIS 2019.

     

    • Pinchao Liu, Adnan Maruf, Farzana Beente Yusuf, Labiba Jahan, Hailu Xu, Boyuan Guan, Liting Hu and Sitharama S. Iyengar. “Towards Adaptive Replication for Hot/Cold Blocks in HDFS using MemCached”, In Proceedings of The International Conference on Data Intelligence and Security (ICDIS 2019), June 2019.

    Hailu Xu and Pinchao Liu Participate IEEE CLOUD 2018 at San Francisco

    Hailu and Pinchao presented ELVES lab work “Oases: An Online Scalable Spam Detection System for Social Networks” and “A Toolset for Detecting Containerized Application’s Dependencies in CaaS Clouds” separately in the conference IEEE CLOUD 2018 at San Francisco.

    Two Papers Are Accepted At IEEE CLOUD 2018

    Congratulation to Hailu and Pinchao! Their following papers have been accepted at IEEE  International Conference on Cloud Computing (IEEE CLOUD 2018), July 2-7, 2018, San Francisco, CA, USA.

     

    • Hailu Xu, Liting Hu, Pinchao Liu, Yao Xiao, Wentao Wang, Jai Dayal, Qingyang Wang and Yuzhe Tang, “Oases: An Online Scalable Spam Detection System for Social Networks,” 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, 2018
    • Pinchao Liu, Liting Hu, Hailu Xu, Zhiyuan Shi, Jason Liu, Qingyang Wang, Jai Dayal and Yuzhe Tang, “A Toolset for Detecting Containerized Application’s Dependencies in CaaS Clouds,” 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, 2018