Data Analysis and Intelligent Systems Lab Its research is focusing on: Spatial Data Mining Clustering and Anomaly Detection Classification and Prediction GIS Current Projects Clustering Algorithms with Plug-in Fitness Functions and Other Non-Traditional Clustering Approaches Analyzing and Doing Useful Things with Bio-aerosol Data Interestingness Scoping Algorithms for the Analysis of Spatial and Spatio-temporal Datasets Using Mixture Models for Anomaly Detection and Change Analysis Taxonomy Generation—Learning Class Hierarchies from Training Data Educational Data Mining (lead by Nouhad Rizk) UH-DAIS

Research Areas and Projects Data Analysis and Intelligent Systems Lab (UH-DAIS) Its research is focusing on: Data Mining Data Science and Making Sense of Data GIS (Geographical Information Systems) AI (Artificial Intelligence) 2. Current Projects Clustering Algorithms with Plug-in Fitness Functions, other Non-Traditional Clustering and Hotspot Discovery (HD) Approaches Predicting and Understanding Flooding Educational Data Mining (jointly with Nouhad Rizk) Taxonomy Generation—Learning Class Hierarchies from Training Data Fast Execution Frameworks for Agglomerative Algorithms just starting Critical Infrastructure Resilience Artificial Intelligence Planning, particularly Disaster Planning just starting UH-DAIS

1a. Non-Traditional Clustering/HD/ED Algorithms Mining Spatial & Spatio-Temporal Datasets Clustering Algorithms With plug-in Fitness Functions Agglomerative IHD Approaches MOSAIC STAXAC Agglomerative Clustering Algorithms AVALANCHE Spatio-Temporal Event Detection ST Clustering Prototype-based Clustering Parallel Computing CLEVER Finding High Correlation Hotspots Ozone/PM2.5 UH-DAIS

1b. Interestingness Hotspot Discovery Objective: Find interesting contiguous regions in spatial data sets based on the domain expert’s notion of interestingness which is captured in an interestingness function Methodology: Identify hotspot seeds Grow seeds by adding neighboring objects Remove redundant hotspots using a graph-based approach Find Scope of hotspots (polygonal boundary detection) Data sets: Gridded, polygonal, point-based data sets People: Fatih Akdag

1.c Spatio-Temporal Clustering Remark: Future Research will also investigate Spatio-Temporal Event Detection Analyzing NYC Cab Pickup Data People: Yongli Zhang and Karima Elgarroussi UH-DAIS

1d: Spatio-temporal Event Detection Example: Event Detection System Architecture

2. Predicting and Understanding Flooding ResearchTasks: Predict Water Levels Flood Vulnerability Mapping Development of Flood Planning Systems Flood Early Warning Systems People: Christariny Hutapea, Yongli Zhang, Romita Banerjee and Yue Cao http://www.harriscountyfws.org/ UH-DAIS

a. A Generic DAG-Based Chaining Approach for WLP R(t),R(t-1),…(Rainfall) W(t), W(t-1),…(Water-level) V(t),V(t-1) (Stream Velocity) S(t), S(t-1) (Soil Moisture) Raw Data DAG of Measuring Points Prediction Scenario Mapping Tool currently under development at UH Model Execution Framework Data Sets (one for each Measuring Point) Single Target Prediction System uses f2 f1 f3 f4 DAG Models (one for each Measuring Point) off the shelf UH-DAIS

b. Automated Generation of Flood Risk Maps UH-DAIS

3. Educational Data Mining (EDM) People: Nouhad Rizk, Karthik Bibireddy, Rohith Jidagam and Alex Lam UH-DMML

4. Taxonomy Generation Goals: Organizing datasets hierarchically People: Paul Amalaman and Chong Wang Taxonomy Generation Algorithm Datasets Goals: Organizing datasets hierarchically Learning “Interesting” Subclasses UH-DAIS

6. Dr. Eick’s Resilience Work In 2015, I was the PI of a large funded DHS project in which we designed an early warning system for bio-threats using “cheap” biosensors. I am part of a large research group at UH whose research centers on “Making the Houston Harbor Smart and Resilient”. Disaster analytics and understanding what makes communities resilient to absorb and recover from disasters. Moreover, my research group is starting new research in critical infrastructure resilience that employs artificial intelligence planning techniques to come up with plans that absorb or recover from failure of critical components. We also work on “new” recovery-based resilience metrics. UH-DAIS

Helping Scientists to Make Sense Out of their Data Figure 1: Co-location regions involving deep and shallow ice on Mars Figure 2: Interestingness hotspots where both income and CTR are high. Figure 3: Maryland Crime Hotspots UH-DAIS

Some UH-DAIS Graduates 1 Christopher T. Ryu, Professor, Department of Computer Science, California State University, Fullerton Dr. Wei Ding, Associate Professor, Department of Computer Science, University of Massachusetts, Boston Sharon M. Tuttle, Professor, Department of Computer Science, Humboldt State University, Arcata, California Sujing Wang, Assistant Professor, Department of Computer Science, Lamar University, Beaumont, Texas Christoph F. Eick

Some UH-DAIS Graduates 2 Puja Anchlia, MS Ebay, San Jose Chun-sheng Chen, Ebay, Seattle Chong Wang, MS Apple Justin Thomas Johns Hopkins University Applied Physics Laboratory Mei-kang Wu MS Microsoft, Bellevue, Washington Jing Wang MS AOL, California Rachsuda Jiamthapthaksin PhD Faculty, Assumption University, Bangkok, Thailand Christoph F. Eick

2016/2017 Students in the UH-DAIS Lab PhD Students: Yongli Zhang, Fatih Akdag, Romita Banerjee and Chong Wang Master Students: Riny Hutapea, Yue Cao, Karthik Bibireddy and Priyal Kulkarni Undergraduate Students: Alex—Lester Moreira Cruz and Hasnain Bilgrami Visiting and Externally Advised Students: Karima Elgarroussi Contributing Alumni: Paul Amalaman and Sujing Wang UH-DAIS