Life sciences researchers perform scientific literature search as part of their daily activities. Many such searches are executed against PubMed, a central repository of life sciences articles, and often return hundreds, or even thousands, of results, pointing to the need for data exploration tools. In this demonstration we present SkylineSearch, a semantic ranking and result visualization system designed specifically for PubMed, and available to the scientific community at skyline.cs.columbia.edu. Our system leverages semantic annotations of articles with terms from the MeSH controlled vocabulary, and presents results as a two-dimensional skyline, plotting relevance against publication date. We demonstrate that SkylineSearch supports a richer data exploration experience than does the search functionality of PubMed, allowing users to find relevant references more easily. We also show that SkylineSearch executes queries and presents results in interactive time.

An ever-increasing amount of data and semantic knowledge in the domain of life sciences is bringing about new data management challenges. In this paper we focus on adding the semantic dimension to literature search, a central task in scientific research. We focus our attention on PubMed, the most significant bibliographic source in life sciences, and explore ways to use high-quality semantic annotations from the MeSH vocabulary to rank search results. We start by developing several families of ranking functions that relate a search query to a document's annotations. We then propose an efficient adaptive ranking mechanism for each of the families. We also describe a two-dimensional Skyline-based visualization that can be used in conjunction with the ranking to further improve the user's interaction with the system, and demonstrate how such Skylines can be computed adaptively and efficiently. Finally, we evaluate the effectiveness of our ranking with a user study.

Database processes must be cache-efficient to effectively utilize modern hardware. In this paper, we analyze the importance of temporal locality and the resultant cache behavior in scheduling database operators for in-memory, block oriented query processing. We demonstrate how the overall performance of a workload of multiple database operators is strongly dependent on how they are interleaved with each other. Longer time slices combined with temporal locality within an operator amortize the effects of the initial compulsory cache misses needed to load the operator's state, such as a hash table, into the cache. Though running an operator to completion over all of its input results in the greatest amortization of cache misses, this is typically infeasible because of the large intermediate storage requirement to materialize all input tuples to an operator. We show experimentally that good cache performance can be obtained with smaller buffers whose size is determined at runtime. We demonstrate a low-overhead method of runtime cache miss sampling using hardware performance counters. Our evaluation considers two common database operators with state: aggregation and hash join. Sampling reveals operator temporal locality and cache miss behavior, and we use those characteristics to choose an appropriate input buffer/block size. The calculated buffer size balances cache miss amortization with buffer memory requirements.