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Data uncertainty is inherent in applications such as sensor monitoring systems, location-based services, and biological databases. To manage this vast amount of imprecise information, probabilistic databases have been recently developed. In this paper, we study the discovery of <i>frequent patterns and association rules</i> from probabilistic data under the(More)
Modern query engines are increasingly being required to process enormous datasets in near real-time. While much can be done to speed up the data access, a promising technique is to reduce the need to access data through data skipping. By maintaining some metadata for each block of tuples, a query may skip a data block if the metadata indicates that the(More)
Graphs can be found in applications like social networks, bibliographic networks, and biological databases. Understanding the relationship, or links, among graph nodes enables applications such as link prediction, recommendation, and spam detection. In this paper, we propose link-based similarity join (LS-join), which extends the similarity join operator to(More)
The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. In this paper, we study how the Voronoi diagram can be used on uncertain data, which are inherent in scientific and business applications. In particular, we propose the Uncertain-Voronoi Diagram (or UV-diagram in short). Conceptually, the data space(More)
The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. We study how the Voronoi diagram can be used for uncertain spatial data, which are inherent in scientific and business applications. Specifically, we propose the Uncertain-Voronoi diagram (or UV-diagram), which divides the data space into disjoint(More)
The availability of rich data from sources such as the World Wide Web, social media, and sensor streams is giving rise to a range of applications that rely on a clean, consistent, and integrated database built over these sources. Human input, or crowd-sourcing, is an effective tool to help produce such high-quality data. It is infeasible, however, to(More)
We propose to demonstrate a fine-grained partitioning framework that reorganizes the data tuples into small blocks at data loading time. The goal is to enable queries to maximally skip scanning data blocks. The partition framework consists of four steps: (1) workload analysis, which extracts features from a query workload, (2) augmentation, which augments(More)
EUK4010 has been identified to exhibit an inhibitory effect on beta-amyloid (Abeta)(1-42)-induced loss of neuronal cell viability. Further studies demonstrated that EUK4010 attenuated the Abeta(1-42)-induced degeneration in both cultured rat hippocampal neurons and human neuroblastoma cells, as demonstrated by typical morphological changes, cell viability(More)
As data volumes continue to grow, modern database systems increasingly rely on data skipping mechanisms to improve performance by avoiding access to irrelevant data. Recent work [39] proposed a fine-grained partitioning scheme that was shown to improve the opportunities for data skipping in row-oriented systems. Modern analytics and big data systems(More)
Multi-pulse signals are composed of finite pulse streams of arbitrary pulse shape. With the pulse shape known, a multichannel sampling scheme for multi-pulse signals can operate at the rate of innovation, which is much lower than the Nyquist rate. The sampling system is based on low-pass filters, oscillators and integrators. By now there is no hardware to(More)