Time-adaptive sketches (Ada sketches) for summarizing data streams

Time-adaptive sketches (Ada Sketches) for Summarizing Data Streams Shrivastava et al. SIGMOD 2016 More algorithm fun today, and again in the context of data streams. It’s the 3 V’s of big data, but not as you know it: Volume, Velocity, and Var… Volatility. Volatility here refers to changing patterns in the data over time, and … Continue reading Time-adaptive sketches (Ada sketches) for summarizing data streams

Sharing-aware outlier analytics over high-volume data streams

Sharing-aware outlier analytics over high-volume data streams Cao et al. SIGMOD 2016 With yesterday’s preliminaries on skyline queries out of the way, it’s time to turn our attention to the Sharing-aware Outlier Processing (SOP) algorithm of Cao et al. The challenge that SOP addresses is that of building a stream-based outlier detection system that can … Continue reading Sharing-aware outlier analytics over high-volume data streams

Progressive skyline computation in database systems

Progressive skyline computation in database systems Papadias et al. SIGMOD 2003 I’m still working through some of the papers from SIGMOD 2016 (as some of you spotted, that was the unifying them for last week). But today I’m jumping back to 2003 to provide some context for a streaming analytics paper we’ll be looking at … Continue reading Progressive skyline computation in database systems

Spheres of influence for more effective viral marketing

Spheres of influence for more effective viral marketing Mehmood et al. SIGMOD ’16 In viral marketing the idea is to spread awareness of a brand or campaign by exploiting pre-existing social networks. The received wisdom is that by targeting a few influential individuals, they will be able to spread your marketing message to a large … Continue reading Spheres of influence for more effective viral marketing

DBSherlock: A performance diagnostic tool for transactional databases

DBSherlock: A performance diagnostic tool for transactional databases Yoon et al. SIGMOD ’16 …tens of thousands of concurrent transactions competing for the same resources (e.g. CPU, disk I/O, memory) can create highly non-linear and counter-intuitive effects on database performance. If you’re a DBA responsible for figuring out what’s going on, this presents quite a challenge. … Continue reading DBSherlock: A performance diagnostic tool for transactional databases