Bounded Graph Pattern Matching With View

Bounded evaluation using views is to compute the answers $Q({\cal D})$ to a query $Q$ in a dataset ${\cal D}$ by accessing only cached views and a small fraction $D_Q$ of ${\cal D}$ such that the size $|D_Q|$ of $D_Q$ and the time to identify $D_Q$ are independent of $|{\cal D}|$, no matter how big ${\cal D}$ is. Though proven effective for relational data, it has yet been investigated for graph data. In light of this, we study the problem of bounded pattern matching using views. We first introduce access schema ${\cal C}$ for graphs and propose a notion of joint containment to characterize bounded pattern matching using views. We show that a pattern query $\sq$ can be boundedly evaluated using views ${\cal V}(G)$ and a fraction $G_Q$ of $G$ if and only if the query $\sq$ is jointly contained by ${\cal V}$ and ${\cal C}$. Based on the characterization, we develop an efficient algorithm as well as an optimization strategy to compute matches by using ${\cal V}(G)$ and $G_Q$. Using real-life and synthetic data, we experimentally verify the performance of these algorithms, and show that (a) our algorithm for joint containment determination is not only effective but also efficient; and (b) our matching algorithm significantly outperforms its counterpart, and the optimization technique can further improve performance by eliminating unnecessary input.

Making big data small

Big data analytics is often prohibitively costly and is typically conducted by parallel processing with a cluster of machines. Is big data analytics beyond the reach of small companies that can only afford limited resources? This paper tackles this question by presenting Boundedly EvAlable SQL ( BEAS ), a system for querying big relations with constrained resources. The idea is to make big data small. To answer a query posed on a dataset, it often suffices to access a small fraction of the data no matter how big the dataset is. In the light of this, BEAS answers queries on big data by identifying and fetching a small set of the data needed. Under available resources, it computes exact answers whenever possible and otherwise approximate answers with accuracy guarantees. Underlying BEAS are principled approaches of bounded evaluation and data-driven approximation, the focus of this paper.