23 research outputs found
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Improving the Effectiveness of Answering Keyword Queries Using Effective Subsets
Most database users do not know formal query languages, such as SQL, and prefer to express their information needs using usable query languages, such as keyword queries. Keyword queries, however, are inherently ambiguous and challenging for the database systems to understand and answer effectively. We propose a novel approach to improving the effectiveness of answering keyword queries by processing.
them over subsets of the database, called effective subsets. Effective subsets contain
only the most promising candidate answers for these queries. We have shown that
our method significantly improves the effectiveness of answering keyword queries
using extensive empirical study
PrismDB: Read-aware Log-structured Merge Trees for Heterogeneous Storage
In recent years, emerging hardware storage technologies have focused on
divergent goals: better performance or lower cost-per-bit of storage.
Correspondingly, data systems that employ these new technologies are optimized
either to be fast (but expensive) or cheap (but slow). We take a different
approach: by combining multiple tiers of fast and low-cost storage technologies
within the same system, we can achieve a Pareto-efficient balance between
performance and cost-per-bit.
This paper presents the design and implementation of PrismDB, a novel
log-structured merge tree based key-value store that exploits a full spectrum
of heterogeneous storage technologies (from 3D XPoint to QLC NAND). We
introduce the notion of "read-awareness" to log-structured merge trees, which
allows hot objects to be pinned to faster storage, achieving better tiering and
hot-cold separation of objects. Compared to the standard use of RocksDB on
flash in datacenters today, PrismDB's average throughput on heterogeneous
storage is 2.3 faster and its tail latency is more than an order of
magnitude better, using hardware than is half the cost