Catching the heartbeat of the data management community: perspectives on exciting and controversial topics, from and for the ACM SIGMOD community.
In this blog, we discuss the potential benefits of augmenting automated view recommendation solutions with query refinement techniques towards achieving insightful data exploration. Particularly, effective data exploration has been fueled by many approaches that rely on either view recommendation…
Read more…Uncertainty arises naturally in many application domains due to measurement errors, human error in data entry or transformation, missing data and bias in data collection, and many other reasons. When uncertainty is ignored during data preprocessing and analysis, this leads to hard to trace errors…
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Similarity search is a fundamental building block for a myriad of critical data science applications involving large collections of high-dimensional objects, including data discovery, data cleaning, information retrieval, classification, outlier detection and clustering. Similarity search finds…
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Given a large number of users’ preferences (numerical or ordinal scores, ranked order) over a large number of objects, returning top-k results entails selecting a small list/set containing exactly k objects that are most “appropriate “. In this article, I will investigate two alternatives for…
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The sheer volume, variety, and velocity of data in this modern era have enabled significant advancements in many research areas. However, the advancements in the research community thanks to Big Data do not necessarily translate to the benefit of society; of ordinary people living ordinary lives.…
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In this post, we motivate the need for efficient and effective solutions for data series similarity search, and we briefly present the work that has been done in this direction by the data series community. We also discuss the relationship to high-dimensional (high-d) vectors and deep neural…
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We built a tool, EpiPolicy, to help policy-makers better plan interventions to combat epidemics [13]. It was an eye-opening experience, where through collaborations and interviews with teams of epidemiologists, public health officials, and economists, we understood some of the complexities of…
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In 2009, we wrote an article highlighting some database challenges in a co-space environment [1]. In such an environment, the physical space and the digital space co-exist in a “universe” and applications can manipulate the data flow within and across the two spaces. 13 years have since passed and…
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Financially, poor data quality costs organizations some ludicrous amounts of money. Worse, poor data quality is a strong inhibitor to the success of data science: No analytical method can create value from poor quality data. As a consequence, data science projects invest a majority of their…
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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…
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