BPI cluster meeting – Participation behavior and social welfare in repeated task allocations

7th December 12:30-13:30 at Pav K.16 we have BPI cluster meeting.

Qing Chuan (Charlie) Ye from  Erasmus University Rotterdam will present his work on
Participation behavior and social welfare in repeated task allocations


Task allocation problems have focused on achieving one-shot optimality. In practice, many task allocation problems are of repeated nature, where the allocation outcome of previous rounds may influence the participation of agents in subsequent rounds, and consequently, the quality of the allocations in the long term. We investigate how allocation influences agents’ decision to participate using prospect theory, and simulate how agents’ participation affects the system’s long term social welfare. We compare two task allocation algorithms in this study, one only considering optimality in terms of costs and the other considering optimality in terms of primarily fairness and secondarily costs. The simulation results demonstrate that fairness incentivizes agents to keep participating and consequently leads to a higher social welfare.

Everyone is invited!


“Data Science from Different Angles” workshop

In the occasion of Julia Kiseleva’s PhD defense, there is a one day workshop “Data Science from Different Angles” at Eindhoven University of Technology on June 13. The invited speakers are Charles L.A. Clarke (University of Waterloo), Djoerd Hiemstra (University of Twente), Alexander Tuzhilin (NYU),  Wil van der Aalst (TU/e), Arjen P. De Vries (RUN) and Maarten de Rijke (UVA).


Please check the full program and register using the following link https://goo.gl/0dSAOR

BPi seminars are back!

BPI seminars will return after the winter break.

They will be held on the first Wednesday of the month, 12:30-13:00 in Pav K.16.

The next seminar is on May 4th, when Vasiliki Arvaniti will talk about “Data mining journal entries for discovering and analyzing unusual financial transactions”

We start also BPI discussion meetings.

They will be held on the third Wednesday of the month, 12:30-13:30 in Pav K.16.

Next meeting is on May 18th.

Everyone is welcome!




DSC/e Lecture Series

DSC/e Lecture Series: 11th November  (12:30-13:30) @ Grand Café ‘De Zwarte Doos’

Making your data “talk” to you:  linguistically conditioned models from data
by prof. dr. ir. Uzay Kaymak


With the abundance of data, many people concentrate on output-related properties of models, such as the accuracy. As long as the system, which processes the data, returns outcomes that are somehow acceptable (e.g. accurate), the underlying nature of the model that the system implements is considered secondary. Indeed, the system can learn from data, adapt itself to the data and hence “make the data talk”. However, natural language is an effective means for the end users to interact with the systems. Hence, there are advantages if our models from data can be conditioned on linguistic information. In this presentation, we consider models whose behavior can be understood in linguistic terms. The models typically return a set of linguistic rules or linguistic descriptions, which can be communicated to the user linguistically. This provides an additional means for the users to interact with the models learned from data.

Registration and more details: http://www.eventbrite.nl/e/dsce-lecture-series-november-11th-2015-uzay-kaymak-registration-16118399567



Next BPI cluster meeting – Friday 30th October

The BPI cluster meetings are taking place on the 5th Friday of the month, from 12:30 to 13:30.

Next meeting is on Friday, 30th October  12:30-13:30, Pav K16

Max van Rooijen will give presentation about evolving fuzzy system for printed circuit board (PCBA) demand forecasting.

Abstract. The author investigates the use of using an evolving fuzzy system for printed circuit board (PCBA) demand forecasting. The algorithm is based on the evolving Takagi–Sugeno (eTS) fuzzy system, which has the ability to incorporate new patterns by changing its internal structure in an on–line fashion. We argue that these capabilities could aid in forecasting dynamic demand patterns such as those experienced in the electronic manufacturing (EMS) industry. An eTS fuzzy system is implemented in the R statistical programming language and is tested on both synthetic and real–world data. To our knowledge, this is one of the first applications of an evolving fuzzy system to forecast product demand. The results indicate that the evolving fuzzy system outperforms competing approaches for the application considered.


Next BPI cluster meeting is on Friday 29th January.

2015 IEEE International Conference on Big Data (IEEE Big Data 2015)

Call for Papers
2015 IEEE International Conference on Big Data (IEEE Big Data 2015)
Oct 29-Nov 1 2015, Santa Clara, CA, USA

In recent years, “Big Data” has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The IEEE Big Data has established itself as the top tier research conference in Big Data. The first conference IEEE Big Data 2013 ( http://cci.drexel.edu/bigdata/bigdata2013/ , regular paper acceptance rate: 17.0%) was held in Santa Clara , CA from Oct 6-9, 2013 with more than 400 registered participants from 40 countries. The IEEE Big Data 2014 (http://cci.drexel.edu/bigdata/bigdata2014/index.htm, regular paper acceptance rate: 18.5.0%) was held in Washington DC, Oct 27-30, 2014 with more than 600 registered participants from 45 countries. The 2015 IEEE International Conference on Big Data (IEEE Big Data 2015) will continue the success of the previous IEEE Big Data conferences. It will provide a leading forum for disseminating the latest research in Big Data Research, Development, and Applications.
We solicit high-quality original research papers (including significant work-in-progress) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity) relevant to variety of data (scientific and engineering, social, sensor/IoT/IoE, and multimedia-audio, video, image, etc) that contribute to the Big Data challenges. This includes but is not limited to the following:

1. Big Data Science and Foundations
a. Novel Theoretical Models for Big Data
b. New Computational Models for Big Data
c. Data and Information Quality for Big Data
d. New Data Standards

2. Big Data Infrastructure
a. Cloud/Grid/Stream Computing for Big Data
b. High Performance/Parallel Computing Platforms for Big Data
c. Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
d. Energy-efficient Computing for Big Data
e. Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
f. Software Techniques andArchitectures in Cloud/Grid/Stream Computing
g. Big Data Open Platforms
h. New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
i. Software Systems to Support Big Data Computing

3. Big Data Management
a. Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
b. Algorithms and Systems for Big DataSearch
c. Distributed, and Peer-to-peer Search
d. Big Data Search Architectures, Scalability and Efficiency
e. Data Acquisition, Integration, Cleaning, and Best Practices
f. Visualization Analytics for Big Data
g. Computational Modeling and Data Integration
h. Large-scale Recommendation Systems and Social Media Systems
i. Cloud/Grid/Stream Data Mining- Big Velocity Data
j. Link and Graph Mining
k. Semantic-based Data Mining and Data Pre-processing
l. Mobility and Big Data
m. Multimedia and Multi-structured Data- Big Variety Data

4. Big Data Search and Mining
a. Social Web Search and Mining
b. Web Search
c. Algorithms and Systems for Big Data Search
d. Distributed, and Peer-to-peer Search
e. Big Data Search Architectures, Scalability and Efficiency
f. Data Acquisition, Integration, Cleaning, and Best Practices
g. Visualization Analytics for Big Data
h. Computational Modeling and Data Integration
i. Large-scale Recommendation Systems and Social Media Systems
j. Cloud/Grid/StreamData Mining- Big Velocity Data
k. Link and Graph Mining
l. Semantic-based Data Mining and Data Pre-processing
m. Mobility and Big Data
n. Multimedia and Multi-structured Data- Big Variety Data

5. Big Data Security & Privacy
a. Intrusion Detection for Gigabit Networks
b. Anomaly and APT Detection in Very Large Scale Systems
c. High Performance Cryptography
d. Visualizing Large Scale Security Data
e. Threat Detection using Big Data Analytics
f. Privacy Threats of Big Data
g. Privacy Preserving Big Data Collection/Analytics
h. HCI Challenges for Big Data Security & Privacy
i. User Studies for any of the above
j. Sociological Aspects of Big Data Privacy

6. Big Data Applications
a. Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
b. Big Data Analytics in Small Business Enterprises (SMEs),
c. Big Data Analytics in Government, Public Sector and Society in General
d. Real-life Case Studies of Value Creation through Big Data Analytics
e. Big Data as a Service
f. Big Data Industry Standards
g. Experiences with Big Data Project Deployments

The Industrial Track solicits papers describing implementations of Big Data solutions relevant to industrial settings. The focus of industry track is on papers that address the practical, applied, or pragmatic or new research challenge issues related to the use of Big Data in industry. We accept full papers (up to 10 pages) and extended abstracts (2-4 pages).

Student Travel Award
IEEE Big Data 2015 will offer as many student travel awards as possible to student authors (including post-doc) (IEEE Big Data 2014 –35 student travel awards, IEEE Big Data 2013 – 17 student travel awards)

Conference Co-Chairs:
Dr. Laura Hass, IBM Research Accelerated Discovery Lab, USA
Prof. Vipin Kumar, University of Minnesota, USA

Program Co-Chairs:
Dr. Howard Ho, IBM Amerdan Research Center, USA
Prof. Beng Chin Ooi, National University of Singapore, Singapore
Prof. Prof. Mohammed J. Zaki, Qatar Computing Research Institute, Qatar,
Rensselaer Polytechnic Institute, USA
Industry and Government Program Committee Chair
Dr. Morris Hui-I Hsiao, Institute for Information Industry, Taiwan
Dr. Jian Li, Huawei Technologies Co. Ltd, USA
Dr. Sudarsan Rachuri, National Institute of Standard and Technology, USA
Dr. Shipeng Yu, LinkedIn, USA

BigData Steering Committee Chair:
Prof. Xiaohua Tony Hu, Drexel University, USA, thu@cis.drexel.edu

Paper Submission:
Please submit a full-length paper (upto 10 page IEEE 2-column format) through the online submission system.
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (see link to “formatting instructions” below).

Formatting Instructions
8.5″ x 11″ (DOC, PDF)
LaTex Formatting Macros

Important Dates:
Electronic submission of full papers: July 1, 2015
Notification of paper acceptance: Sept 4, 2015
Camera-ready of accepted papers: Sept 25, 2015
Conference: October 29-Nov 1, 2015

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