Hoyoung Jeung

Systems, Applications & Products in Data Processing (Australia)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Karl Aberer

École Polytechnique Fédérale de Lausanne

Xiaofang Zhou

Hong Kong University of Science and Technology

Man Lung Yiu

Hong Kong Polytechnic University

Heng Tao Shen

University of Electronic Science and Technology of China

Christian S. Jensen

Aalborg University

Zhixian Yan

Samsung (United States)

Saket Sathe

Amazon (Germany)

ÓC

Óscar Corcho

Universidad Politécnica de Madrid

Jean-Paul Calbimonte

HES-SO University of Applied Sciences and Arts Western Switzerland

Asadul Islam

Systems, Applications & Products in Data Processing (Australia)

Cooperative Institutions

École Polytechnique Fédérale de Lausanne

Systems, Applications & Products in Data Processing (Australia)

University of Electronic Science and Technology of China

University of Queensland

National University of Singapore

Chinese Academy of Sciences

École Normale Supérieure - PSL

Queensland University of Technology

Laboratoire d'Informatique Fondamentale de Lille

University of Hong Kong

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Enabling Query Technologies for the Semantic Sensor Web

International Journal on Semantic Web and Information Systems (2012)

Jean-Paul Calbimonte Hoyoung Jeung Óscar Corcho Karl Aberer

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes.

10.4018/jswis.2012010103

Cite

Citations (142)

Advanced analytics on SAP HANA: Churn risk scoring using call network analysis

Shane Bracher Mark H. Holmes Liam Mischewski Asadul Islam Michael McClenaghan

We present a demonstration based on SAP HANA to show a novel approach to churn risk scoring. Our focus is customer retention within a telecommunications setting. The purpose of this demonstration is to help identify customers who should be targeted for a customer retention marketing campaign. The data analysis considers multiple factors - churn likelihood (based on incoming and outgoing communications), customer influence (based on social connections) and the average revenue per customer. The results are presented using skyline visualization and advanced UI techniques to easily and intuitively interpret the analysis.

Skyline

Business Intelligence

10.1109/icde.2015.7113386

Cite

Citations (0)

Trajectory Pattern Mining

Springer eBooks (2011)

Hoyoung Jeung Man Lung Yiu Christian S. Jensen

10.1007/978-1-4614-1629-6_5

Cite

Citations (63)

A Hybrid Prediction Model for Moving Objects

Hoyoung Jeung Qing Liu Heng Tao Shen Xiaofang Zhou

Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an object's movements are more complicated than what the mathematical formulas can represent. Prediction based on an object's trajectory patterns is a powerful way and has been investigated by several work. But their main interest is how to discover the patterns. In this paper, we present a novel prediction approach, namely The Hybrid Prediction Model, which estimates an object's future locations based on its pattern information as well as existing motion functions using the object's recent movements. Specifically, an object's trajectory patterns which have ad-hoc forms for prediction are discovered and then indexed by a novel access method for efficient query processing. In addition, two query processing techniques that can provide accurate results for both near and distant time predictive queries are presented. Our extensive experiments demonstrate that proposed techniques are more accurate and efficient than existing forecasting schemes.

10.1109/icde.2008.4497415

Cite

Citations (277)

Social and Sensor Data Fusion in the Cloud

Surender Reddy Yerva Jonnahtan Saltarin Hoyoung Jeung Karl Aberer

This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice - a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependant people's mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud.

Sensor Fusion

10.1109/mdm.2012.52

Cite

Citations (20)

An Evaluation of Model-Based Approaches to Sensor Data Compression

IEEE Transactions on Knowledge and Data Engineering (2012)

Quoc Viet Hung Nguyen Hoyoung Jeung Karl Aberer

As the volumes of sensor data being accumulated are likely to soar, data compression has become essential in a wide range of sensor-data applications. This has led to a plethora of data compression techniques for sensor data, in particular model-based approaches have been spotlighted due to their significant compression performance. These methods, however, have never been compared and analyzed under the same setting, rendering a "right" choice of compression technique for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the model-based compression techniques. Specifically, we reimplemented several state-of-the-art methods in a comparable manner, and measured various performance factors with our benchmark, including compression ratio, computation time, model maintenance cost, approximation quality, and robustness to noisy data. We then provide in-depth analysis of the benchmark results, obtained by using 11 different real data sets consisting of 346 heterogeneous sensor data signals. We believe that the findings from the benchmark will be able to serve as a practical guideline for applications that need to compress sensor data.

Benchmark (surveying)

Robustness

10.1109/tkde.2012.237

Cite

Citations (76)

Mining Trajectory Patterns Using Hidden Markov Models

Lecture notes in computer science (2007)

Hoyoung Jeung Heng Tao Shen Xiaofang Zhou

10.1007/978-3-540-74553-2_44

Cite

Citations (66)

Cloud based social and sensor data fusion

Surender Reddy Yerva Hoyoung Jeung Karl Aberer

As mobile cloud computing facilitates a wide spectrum of smart applications, the need for fusing various types of data available in the cloud grows rapidly. In particular, social and sensor data lie at the core in such applications, but typically processed separately. This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice — a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependent people's mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud.

Sensor Fusion

Source

Cite

Citations (33)

A GML Data Storage Method for Spatial Databases

Spatial Information Research (2004)

Hoyoung Jeung Soohong Park

Source

Cite

Citations (4)

Maritime Data Management and Analytics: A Survey of Solutions Based on Automatic Identification System

CRC Press eBooks (2017)

Baljeet Malhotra Hoyoung Jeung Thomas Kister Stéphane Bressan Kian‐Lee Tan

This chapter offers an in-depth survey of the research proposals that have exploited maritime data specifically generated by the Automatic Identification System (AIS) to propose applications and data-analytic solutions for various problems. It aims to compile and critique a comprehensive list of such research works, which can serve as an important basis in building new applications as well as offering a useful source of reference for various stakeholders. The stakeholders include port authorities, shipping companies, researchers, maritime practitioners, and law-enforcement agencies. The chapter summarizes the main ideas and techniques of the existing proposals while discussing the advantages and disadvantages of each proposal. It focuses on the maritime data generated by the AIS and their applications in data analytics. The chapter presents an overview of the AIS and its main functions, followed by a brief description of an AIS station that the authors have set up at SAP Research in Singapore.

Identification

Data Analysis

10.1201/b15479-11

Cite

Citations (0)