51,690 research outputs found
Data Sketches for Disaggregated Subset Sum and Frequent Item Estimation
We introduce and study a new data sketch for processing massive datasets. It
addresses two common problems: 1) computing a sum given arbitrary filter
conditions and 2) identifying the frequent items or heavy hitters in a data
set. For the former, the sketch provides unbiased estimates with state of the
art accuracy. It handles the challenging scenario when the data is
disaggregated so that computing the per unit metric of interest requires an
expensive aggregation. For example, the metric of interest may be total clicks
per user while the raw data is a click stream with multiple rows per user. Thus
the sketch is suitable for use in a wide range of applications including
computing historical click through rates for ad prediction, reporting user
metrics from event streams, and measuring network traffic for IP flows.
We prove and empirically show the sketch has good properties for both the
disaggregated subset sum estimation and frequent item problems. On i.i.d. data,
it not only picks out the frequent items but gives strongly consistent
estimates for the proportion of each frequent item. The resulting sketch
asymptotically draws a probability proportional to size sample that is optimal
for estimating sums over the data. For non i.i.d. data, we show that it
typically does much better than random sampling for the frequent item problem
and never does worse. For subset sum estimation, we show that even for
pathological sequences, the variance is close to that of an optimal sampling
design. Empirically, despite the disadvantage of operating on disaggregated
data, our method matches or bests priority sampling, a state of the art method
for pre-aggregated data and performs orders of magnitude better on skewed data
compared to uniform sampling. We propose extensions to the sketch that allow it
to be used in combining multiple data sets, in distributed systems, and for
time decayed aggregation
An efficient closed frequent itemset miner for the MOA stream mining system
Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version
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