HINCare: An Intelligent Community Support and Timebanking System

HINCare uses HIN (Heterogeneous Information Network) to recommend helpers to elderly or other service recipients. HIN is a large network database that stores the relationship information among elderly, helpers, and NGOs. We use the HIN to find out which helpers are the best candidates to assist elderly / service recipients. The algorithms that use HINs and AI technologies for matching elderly and helpers are based on our recent research results. This is the first time that HIN is used to support elderly and community care.



Data Science for Social Good, or DSSG, broadly refers to the use of data engineering and analysis solutions in the social work domain.  I am interested in this field, because it gives me a chance to understand how database technologies can be used in a domain whose data-driven approaches are only in its infancy. Moreover, we can develop impactful and meaningful projects that benefit the society at large.

Here, I will share my work in DSSG. First, I will introduce our Social Technology and Research Laboratory (STAR)...


An Indexing Framework
for Queries on Probabilistic Graphs

Information in many applications, such as mobile wireless systems, social networks, and road networks, is captured by graphs. In many cases, such information is uncertain. We study the problem of querying a probabilistic graph, in which vertices are connected to each other probabilistically. In particular, we examine “source-to-target” queries (or ST-queries), such as computing the shortest path between two vertices...


Discovering Meta-Paths

in Large Heterogeneous Information 

The Heterogeneous Information Network (HIN) is a graph data model in which nodes and edges are annotated with class and relationship labels. Large and complex datasets, such as Yago or DBLP, can be modeled as HINs. Recent work has studied how to make use of these rich information sources. In particular, meta-paths, which represent sequences of node classes and edge types between two nodes in a HIN, have been proposed for such tasks as information retrieval, decision making, and product recommendation...


Meta Structure: Computing Relevance in Large Heterogeneous Information Networks

A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures can be used in various applications, including entity resolution, recommendation, and information retrieval...