Catching the heartbeat of the data management community: perspectives on exciting and controversial topics, from and for the ACM SIGMOD community.
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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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…
Read more…Data visualization and analytics are nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and…
Read more…Data visualization and analytics are nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and…
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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…We are witnessing data of unprecedented volume, variety and velocity. Such data is collected from almost every aspect of human activity and stored in large repositories in order to be later analyzed and turned into useful insights. The storage model is not any more the one in which data is placed…
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.…
Read more…I think we need to take a hard look at approximate query processing. Don’t get me wrong. The vision of approximate query processing is indeed compelling. In the age of Big Data, the ability to answer analytic queries “approximately”, but at a fraction of the cost of executing the query in the…
Read more…Google has recently announced that its flagship wide-area database named Spanner has been made available on the Google Cloud. Google Spanner is the next generation globally-distributed database built inside Google and announced to the world through the paper published in OSDI 2012 [1]. This article…
Read more…The big data era (now more than a decade old) ushered in dramatic shift in mentality whereby enterprises increased their appetite for making “data-driven” decisions. To a large extent, most enterprises have gotten very good at collecting data. Now, the next big challenge is to unlock non-trivial…
Read more…In the 21st century, our everyday lives are shaped by what machines tell us to do. Big data and their application to economy, policy, and our social life act as behavioral equalizers. And this they do under the assumption of optimization in both subtle and open ways. In fact, they are pushing…
Read more…Some months ago, I found an exciting piece of news on the Web: a top manager proclaimed [1]: “If the most important thing you offer is data, you are in trouble. (….) Algorithms is where the real value lies. Algorithms define action.” How interesting! It seems to suggest that everybody, including…
Read more…(This blog post is an extended version of an October 12, 2015 Le Monde op-ed article (in French)) Our society is increasingly relying on algorithms in all aspects of its operation. We trust algorithms not only to help carry out routine tasks, such as accounting and automatic manufacturing, but also…
Read more…Big Data, and its 4 Vs – volume, velocity, variety, and veracity – have been at the forefront of societal, scientific and engineering discourse. Arguably the most important 5th V, value, is not talked about as much. How can we make sure that our data is not just big, but also valuable? WebDB…
Read more…Most professional fields, whether in business or academia, rely on data and have done so for centuries. In the digital age and with the emergence of Big Data, this dependency is growing dramatically – perhaps out of proportion to its current value given the concepts, tools, and techniques presently…
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“Both theoretical and empirical research may be unnecessarily complicated by failure to recognize the effects of heterogeneity” – Vaupel & Yashin Big Data is daily topic of conversation among data analysts, with much said and written about its promises and pitfalls. The issue of heterogeneity,…
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Big Data should be Interesting Data! There are various definitions of Big Data; most center around a number of V’s like volume, velocity, variety, veracity – in short: interesting data (interesting in at least one aspect). However, when you look into research papers on Big Data, in SIGMOD, VLDB, or…
Read more…Big Data is the buzzword in the database community these days. Two of the first three blog entries of the SIGMOD blog are on Big Data. There was a plenary research session with invited talks at the 2012 SIGMOD Conference and there will be a panel at the 2012 VLDB Conference. Probably, everything…
Read more…I was recently approached by an entrepreneur who had an interesting way to correlate short term performance of a stock with news reports about the stock. Needless to say, there are many places from which one can get the news, and what results one gets from this sort of analysis does depend on the…
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