Catching the heartbeat of the data management community: perspectives on exciting and controversial topics, from and for the ACM SIGMOD community.
The past few years of generative AI have upended research agendas across academia. Having just spent my sabbatical in the Bay Area, where the San Francisco fog is mixed with a tinge of forest fire and LLMs, I wanted to reflect on the role of the academic database research community within this sea…
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Data as a major component of a deep learning solution is often undervalued in the ML projects, which results in a lower-than-expected accuracy, requiring hours and hours of model tuning. According to Andrew Ng, 99% of the recent publications are model-centric with only 1% being data-centric. He…
Read more…For almost 30 years, the DB / data management community has intensively studied the vexing pains of data integration, cleaning, and transformation. This research has largely been in the contexts of RDBMSs, SQL-oriented business intelligence (BI), and knowledge base construction. But as the emerging…
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The recent return of AI summer and the enthusiastic uptake of AI in the commercial world can be loosely attributed to three innovations: Apple’s Siri, Google’s self-driving cars, and IBM Watson Jeopardy. This enthusiasm stems from the belief that AI will influence a wide range of applications…
Read more…Overview of DEEM 2018 The ACM SIGMOD Second Workshop on Data Management for End-to-End Machine Learning (DEEM) was successfully held last June in Houston, TX. The goal of DEEM is to bring together researchers and practitioners at the intersection of applied machine learning (ML) and data…
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When dealing with real-world data, dirty data is the norm rather than the exception. We continuously need to predict correct values, impute missing ones, and find links between various data artefacts such as schemas and records. We need to stop treating data cleaning as a piecemeal exercise…
Read more…The web is an ever-evolving source of information, with data and knowledge derived from it powering a great range of modern applications. Accompanying the huge wealth of information, web data also introduces numerous challenges due to its size, diversity, volatility, inaccuracy, and contradictions.…
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