Web and Complex Systems Lab

Dept. of Computer Science and Engineering
Kno.e.sis Research Center
Wright State University

About Us

The Web and Complex Systems (WaCS) Laboratory investigates models and learning algorithms for developing and understanding knowledge from complex systems. Our projects span network and relational data analysis, development of statistical learning algorithms and software tools, and applied analyses in contexts such as web, social, and geospatial informatics. Collaborations with researchers in social computing, data semantics, cybersecurity, information systems and operations research are strongly encouraged.

Faculty:
Dr. Derek Doran [derek.doran [at] wright.edu]

Graduate Students:
Ning Xie (PhD)
Mahdieh Zabihimayvan (PhD)
Lakshika Balasuriya (MS)
Jace Robinson (MS)
Matt Piekenbrock (MS) (see article)
Kyle Brown (MS)
Matt Maurice (MS)

Undergraduate Students:
Logan Rickert
Scott Duberstein
Ethan Wolfer
Past Students:
Nathan Rude (MS, 2016)
Samir Yelne (MS, 2016)
Nripesh Trivedi (BS, 2015)

News

6/5/17: A Soft Computing Approach for Benign and Malicious Web Robot Detection accepted in the journal Expert Systems with Applications. The article describes a new approach to detect Web robot sessions from web logs. By fuzzy set theory and Markov clustering, the approach selects an appropriate set of classification features, thereby adapting to server-specific session level patterns automatically. This is Mahdieh's first publication with WaCS!

5/22/17: WaCS student Matt Piekenbrock receives well deserved press from the university. See the article here!

5/5/17: Matt Piekenbrock's proposal to the 2017 Google Summer of Code was accepted! Matt will be funded by Google to implement an open-source R package that unifies and improves algorithms for estimating the empirical cluster tree of a dataset. The package will be a realization of recent, rapid advances in density-based clustering. The proposal selection process is very competitive. Congratulations Matt!

3/7/17: EmojiNet: An Open Service and API for Emoji Sense Discovery, accepted at the AAAI Intl. Conference on Weblogs and Social Media. This work describes the release of our public knowledge base for interpreting the sense, or meaningful interpretation, of emoji when used in a particular context. Applications to emoji sense disambiguation and similarity evluation is discussed. Check out the resource here -- well done Sanjaya and Lakshika!

2/1/17: A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots, accepted at the 2017 Intl. Symposium on Neural Networks. This work introduces our new prefetcher for web resources requested by robots or crawlers, which combines a deep recurrent neural network with Bayesian networks that combine prior global information with session-specific information about a robot. Ning and Kyle are joint first authors on this work!

1/17/17: Dr. Doran's book chapter on Graph/Link Mining has been accepted for inclusion in Springer's upcoming Encyclopedia of Big Data. The chapter covers an introduction to graph mining for a non-technical expert, relates graph mining techniques to the scientific field of network science, and presents other fundamental concepts and graph mining techniques.

12/20/16: Ning Xie wins two scholarships to attend important events for women in computing: the CRA-W Grad Cohort Workshop in Washington, DC and the Ohio Celebration of Women in Computing at Lake Huron in Ohio. Congratulations Ning!

12/15/16: Nathan Rude successfully defends his MS Thesis Intelligent Caching to Mitigate the Impact of Web Robots on Web Servers. He will be taking a job as a Software Engineer for data intensive computing at LexisNexis Special Services this January. **Congratulations Nathan!!**

12/13/16: Samir Yelne successfully defends his MS Thesis Measures of User Interactions, Conversations, and Attacks in a Crowdsourced Platform Offering Emotional Support. He will be taking a job as a Data Scientist at Cisco in San Jose, CA this January. **Congratulations Samir!!**

10/20/16: Keep the Conversation Going: Engagement-Based Customer Segmentation on Online Social Service Platforms, accepted in the journal Information Systems Frontiers. This work integrates kernel functions into the traditional k-means clustering algorithm to segment customers on online platforms having social functions (heavy-tails abound). Nripesh is first author on this work and represents his work during his Summer 2015 WaCS visit!

10/1/16: WaCS is awarded a research award from the Ohio Federal Research Network titled Human Centered Big Data. This project is in collaboration with the DaSE and BiRG labs at Wright State, as well as the Wright State Research Institute, Case Western University, and the Ohio State University.

9/28/16: RFID-Based Information Visibility for Hospital Operations: Exploring its Positive Effects using Discrete Event Simulation, accepted in the journal Health Care Management Science. This article presents a simulation-based performance analysis of hospitals wherein patients visit stations that have visible waiting times. Nathan played a major role in this collaborative work between WaCS and the Dept. of Information Systems and Supply Chain Management.

8/20/16: EmojiNet: Building a Machine Readable Sense Inventory for Emoji, accepted at SocInfo 2016. This paper introduces a new web resource: a machine readable sense inventory for emoji. EmojiNet integrates multiple emoji lexicographic resources found on the Web along with BabelNet, a comprehensive machine readable sense inventory for words, to infer sense definitions. Lakshika is second author on this work and was instrumental in its development! Check out the resource here!

8/15/16: Ning Xie wins an NSF Travel Fellowship to attend the 12th annual Reasoning Web Summer School in Aberdeen, Scotland. The school will develop her knowledge in data semantics and linked data theory and systems. The school may prove useful in future explorations at the intersection of deep learning and data semantics, and in web traffic and linked data analysis.

7/29/16: WaCS welcomes Jace Robinson (MS) and Ethan Wolfer (UGrad) to our research group.

7/14/16: Finding Street Gang Members on Twitter, accepted at IEEE/ACM ASONAM 2016. This paper proposes a system to automatically identify twitter profiles affiliated with street gangs. Its novelty lies in the use of heterogeneous features, including image tags inferred by a deep neural network, tags from YouTube video links, and emoji use, whch were inferred by an analysis of what may be the largest set of twitter profiles related to gang members. Lakshika is first author on this work!

6/22/16: Word Embeddings to Enhance Twitter Gang Member Profile Identification , accepted at IJCAI Workshop on Semantic Machine Learning. This paper discusses the use of deep learning to map text, extracted from a set of heterogeneous features, into a single space for Twitter profile classification. This is Lakshika's first paper!

4/19/16: Measuring the Users and Conversations of a Vibrant Online Emotional Support System , accepted at IEEE Intl. Symposium on Computers and Communications. This paper follows up our ASONAM 2015 work on studying the users of a large-scale emotional support service and the dynamics of the conversations they hold with each other. Students Samir Yelne and Nripesh Trivedi are co-authors on this work.

4/14/16: Matt Piekenbrock is listed as a co-author of the R package dbscan. The package implements a critical non-parametric clustering algorithm and is downloaded at least 2,300 times per month around the world. Download it from CRAN!

2/3/16: Exploring Information-Optimal Network Discretization for Dynamic Network Analysis, accepted at Sunbelt 2016 . This poster discusses how a notion of entropy defined by the structure of temporal networks may be used to guide the discretization of continuous network data. Sunbelt is the flagship conference for the International Network for Social Network Analysis.

12/14/15: A Runner-up Best Teaching Paper Award is given to Teaching the Foundations of Data Science: An Interdisciplinary Approach at the 2015 SIGDSA Business Analytics Congress!

12/14/15: WaCS welcomes Ning Xie (PhD) and Scott Duberstein (UGrad) to our research group.

12/11/15: Operationalizing Central Place and Central Flow Theory With Mobile Phone Data, accepted in the journal Annals of Data Science. This work demonstrates how artifacts explained by Central Place and Central Flow Theory, which are geographic explanations about how regions develop economically and socially, may be unearthed in mobile phone datasets.

11/16/15: WaCS is awarded an REU Supplement to our NSF project. The funds will support Logan Rickert and a new UGRA to be hired through 2017.

10/15/15: Teaching the Foundations of Data Science: An Interdisciplinary Approach, accepted at 2015 SIGDSA Business Analytics Congress! . This paper documents the design, student experiences, and outcomes of a progressive undergraduate course on data analytics for both CS and MIS students co-taught by Dr. Doran. The Congress is a pre-event of ICIS, the largest professional association for information systems.

9/18/15: Request Type Prediction for Web Robot and Internet of Things Traffic , accepted at IEEE ICMLA 2015. Towards building predictive caches for web servers and clouds that can service robot and IoT traffic with better performance, this work motivates the use of recurrent neural networks to anticipate the type of a resource to be requested by bots or IoT devices. This is Nathan's first publication with WaCS!

9/18/15: WAMINet: An Open Source Library for Dynamic Geospace Analysis Using WAMI , accepted at IEEE ISM 2015. We introduce an open source tool for processing WAMI imagery, and its method to derive network models of the dynamics of a monitored geospace. This is Matt M.'s first publication!

8/31/15: A best paper award at IEEE/ACM ASONAM 2015 awarded to Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support!

8/27/15: On the Discovery of Social Roles in Large Scale Social Systems, accepted in the journal Social Network Analysis and Mining. This theoretical work describes an unsupervised, data-driven method to extract the roles of actors in large scale social systems -- an advancement over prior art that defined qualitative analysis or used unscalable mathematical definitions.

7/10/15: Social Media Powered Human Sensing for Smart Cities, accepted in the journal AI Communications. This is an extension of Dr. Doran's 2013 work on social media mining in support of public utilities. This research was carried out in collaboration with industry partners.

6/22/15: Stay Awhile and Listen: User Interactions in a Crowdsourced Platform Offering Emotional Support, accepted at IEEE/ACM ASONAM 2015. This may be the first study that quantitatively measures the behaviors and interactions of people on a large (>100k users) social system designed to offer emotional support to those in need. This is Samir's first publication!

5/12/15: WaCS welcomes Nripesh Trivedi, a visiting student from IIT Varanasi (a top CS school in India), to our research group.


Major Research Projects


Human Centered Big Data (OFRN)
Students: Ning Xie

Understanding the Impact of Web Robot and IoT Traffic on Web Systems (NSF CRII)
Students: Kyle Brown, Logan Rickert

Project Safe Neighborhood: Westwood Partnership to Prevent Juvenile Repeat Offenders (OCJS)
Students: Lakshika Balasuriya

Network Representations and Analytics for Geospatial Analysis (NSF I/UCRC CSR; Air Force Research Labs)
Students: Matt Piekenbrock, Jace Robinson, Ethan Wolfer
WaCS is very thankful to the following organizations for supporting our research and our student's education: