Personalized Point of Interest (POI) recommendation that incorporates users' personal preferences is an important subject of research. However, challenges exist such as dealing with sparse rating data and spatial location factors. As one of the biggest card payment organizations in the United States, our company holds abundant card payment transaction records with numerous features.

Joins are expensive, especially on large data and/or multiple relations. One promising approach in mitigating their high costs is to just return a simple random sample of the full join results, which is sufficient for many tasks. Indeed, in as early as 1999, Chaudhuri et al. posed the problem of sampling over joins as a fundamental challenge in large database systems. They also pointed out a fundamental barrier for this problem, that the sampling operator cannot be pushed through a join, i.e., sample( R bowtie S )≠ sample( R ) bowtie sample( S ). To overcome this barrier, they used precomputed statistics to guide the sampling process, but only showed how this works for two-relation joins.

We present the STORM system to enable spatio-temporal online reasoning and management of large spatio-temporal data. STORM supports interactive spatio-temporal analytics through novel spatial online sampling techniques. Online spatio-temporal aggregation and analytics are then derived based on the online samples, where approximate answers with approximation quality guarantees can be provided immediately from the start of query execution. The quality of these online approximations improve over time. This demonstration proposal describes key ideas in the design of the STORM system, and presents the demonstration plan.

The massive adoption of smart phones and other mobile devices has generated humongous amount of spatial and spatio-temporal data. The importance of spatial analytics and aggregation is ever-increasing. An important challenge is to support interactive exploration over such data. However, spatial analytics and aggregation using all data points that satisfy a query condition is expensive, especially over large data sets, and could not meet the needs of interactive exploration. To that end, we present novel indexing structures that support spatial online sampling and aggregation on large spatial and spatio-temporal data sets. In spatial online sampling, random samples from the set of spatial (or spatio-temporal) points that satisfy a query condition are generated incrementally in an online fashion. With more and more samples, various spatial analytics and aggregations can be performed in an online, interactive fashion, with estimators that have better accuracy over time. Our design works well for both memory-based and disk-resident data sets, and scales well towards different query and sample sizes. More importantly, our structures are dynamic, hence, they are able to deal with insertions and deletions efficiently. Extensive experiments on large real data sets demonstrate the improvements achieved by our indexing structures compared to other baseline methods.

We present a novel adaptive log compression scheme. Results show 30% improvement on compression ratios over existing approaches.

Temporal and multi-version databases are ideal candidates for a distributed store, which offers large storage space, and parallel and distributed processing power from a cluster of (commodity) machines. A key challenge is to achieve a good load balancing algorithm for storage and processing of these data, which is done by partitioning the database. We introduce the concept of optimal splitters for temporal and multi-version databases, which induce a partition of the input data set, and guarantee that the size of the maximum bucket be minimized among all possible configurations, given a budget for the desired number of buckets. We design efficient methods for memory- and disk resident data respectively, and show that they significantly outperform competing baseline methods both theoretically and empirically on large real data sets.