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We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities. Our system design abstracts core tasks of most neural entity linking systems for MonetDB. To the best of our knowledge, this is the first defacto implemented system integrating entity-linking in a database. We leverage the ability of MonetDB to support in-database-analytics with user defined functions (UDFs) implemented in Python. These functions call machine learning libraries for neural text mining, such as TensorFlow. The system achieves zero cost for data shipping and transformation by utilizing MonetDB's ability to embed Python processes in the database kernel and exchange data in NumPy arrays. IDEL represents text and relational data in a joint vector space with neural embeddings and can compensate errors with ambiguous entity representations. For detecting matching entities, we propose a novel similarity function based on joint neural embeddings which are learned via minimizing pairwise contrastive ranking loss. This function utilizes a high dimensional index structures for fast retrieval of matching entities. Our first implementation and experiments using the WebNLG corpus show the effectiveness and the potentials of IDEL., Comment: This manuscript is a preprint for a paper submitted to VLDB2018

Coming high-cadence wide-field optical telescopes will image hundreds of thousands of sources per minute. Besides inspecting the near real-time data streams for transient and variability events, the accumulated data archive is a wealthy laboratory for making complementary scientific discoveries. The goal of this work is to optimise column-oriented database techniques to enable the construction of a full-source and light-curve database for large-scale surveys, that is accessible by the astronomical community. We adopted LOFAR's Transients Pipeline as the baseline and modified it to enable the processing of optical images that have much higher source densities. The pipeline adds new source lists to the archive database, while cross-matching them with the known cataloged sources in order to build a full light-curve archive. We investigated several techniques of indexing and partitioning the largest tables, allowing for faster positional source look-ups in the cross matching algorithms. We monitored all query run times in long-term pipeline runs where we processed a subset of IPHAS data that have image source density peaks over $170,000$ per field of view ($500,000$ deg$^{-2}$). Our analysis demonstrates that horizontal table partitions of declination widths of one-degree control the query run times. Usage of an index strategy where the partitions are densily sorted according to source declination yields another improvement. Most queries run in sublinear time and a few (<20%) run in linear time, because of dependencies on input source-list and result-set size. We observed that for this logical database partitioning schema the limiting cadence the pipeline achieved with processing IPHAS data is 25 seconds., Comment: 16 pages, 5 figures; Accepted for publication in Astronomy & Computing

A core business in the fashion industry is the understanding and prediction of customer needs and trends. Search engines and social networks are at the same time a fundamental bridge and a costly middleman between the customer's purchase intention and the retailer. To better exploit Europe's distinctive characteristics e.g., multiple languages, fashion and cultural differences, it is pivotal to reduce retailers' dependence to search engines. This goal can be achieved by harnessing various data channels (manufacturers and distribution networks, online shops, large retailers, social media, market observers, call centers, press/magazines etc.) that retailers can leverage in order to gain more insight about potential buyers, and on the industry trends as a whole. This can enable the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. In this paper, we provide an overview of the main research challenges and an analysis of the most promising technological solutions that we are investigating in the FashionBrain project.

The query log of a DBMS is a powerful resource. It enables many practical applications, including query optimization and user experience enhancement. And yet, mining SQL queries is a difficult task. The fundamental problem is that queries are symbolic objects, not vectors of numbers. Therefore, many popular statistical concepts, such as means, regression, or decision trees do not apply. Most authors limit themselves to ad hoc algorithms or approaches based on neighborhoods, such as k Nearest Neighbors. Our project is to challenge this limitation. We introduce methods to manipulate SQL queries as if they were vectors, thereby unlocking the whole statistical toolbox. We present three families of methods: feature maps, kernel methods, and Bayesian models. The first technique directly encodes queries into vectors. The second one transforms the queries implicitly. The last one exploits probabilistic graphical models as an alternative to vector spaces. We present the benefits and drawbacks of each solution, highlight how they relate to each other, and make the case for future investigation., Comment: Vision Paper

A group of senior database researchers gathers every few years to assess the state of database research and to point out problem areas that deserve additional focus. This report summarizes the discussion and conclusions of the sixth ad-hoc meeting held May 4-6, 2003 in Lowell, Mass. It observes that information management continues to be a critical component of most complex software systems. It recommends that database researchers increase focus on: integration of text, data, code, and streams; fusion of information from heterogeneous data sources; reasoning about uncertain data; unsupervised data mining for interesting correlations; information privacy; and self-adaptation and repair., Comment: Details of this workshop (presentations and notes) are at http://research.microsoft.com/~gray/lowell/