Archive for the Machine Learning category

Arun Kumar
Arun Kumar

ML/AI Systems and Applications: Is the SIGMOD/VLDB Community Losing Relevance?

Databases, Machine Learning

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 management/systems research to discuss data management/systems issues in ML […]

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Ihab Ilyas
Ihab Ilyas

Data cleaning is a machine learning problem that needs data systems help!

Big Data, Machine Learning, Systems

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 (resolving different types of errors in isolation), and […]

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Azza Abouzied and Paolo Papotti
Azza Abouzied and Paolo Papotti

Courting ML: Witnessing the Marriage of Relational & Web Data Systems to Machine Learning

Big Data, Databases, Interview, Machine Learning

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. This year’s WebDB 2018 theme emphasizes the challenges and opportunities that arise […]

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