DESQ: Frequent Sequence Mining with Subsequence Constraints

Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this paper, we show that many subsequence constraints---including and beyond those considered in the literature---can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive "pattern expressions" to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to compressed finite state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms---although more general---are competitive to existing state-of-the-art algorithms.Comment: Long version of the paper accepted at the IEEE ICDM 2016 conferenc

Methods for frequent sequence mining with subsequence constraints

In this thesis, we study scalable and general purpose methods for mining frequent sequences that satisfy a given subsequence constraint. Frequent sequence mining is a fundamental task in data mining and has many real-life applications like information extraction, market-basket analysis, web usage mining, or session analysis. Depending on the underlying application, we are generally interested in discovering certain frequent sequences, which are described using subsequence constraints. There exists many tools and algorithms for this task, however, they are not sufficiently scalable to deal with large amounts of data that may arise in applications and are generally not extensible across range of applications. We propose scalable, distributed sequence mining algorithms that target MapReduce. Our work builds on MG-FSM, which is a distributed framework for frequent sequence mining. We propose novel algorithms that improve and extend the basic MG-FSM framework to efficiently support traditional subsequence constraints that arise in applications. Additionally, we show that many subsequence constraints---including and beyond the traditional ones considered in literature---can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. To this end, we propose a general purpose framework that provides a set of simple and intuitive ``pattern expressions'', which allows to describe any subsequence constraint of interest and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our experimental study on real-world datasets indicates that our proposed algorithms are scalable and effective across wide range of applications

Closing the gap: Sequence mining at scale

Frequent sequence mining is one of the fundamental building blocks in data mining. While the problem has been extensively studied, few of the available techniques are sufficiently scalable to handle datasets with billions of sequences; such large-scale datasets arise, for instance, in text mining and session analysis. In this article, we propose MG-FSM, a scalable algorithm for frequent sequence mining on MapReduce. MG-FSM can handle so-called “gap constraints”, which can be used to limit the output to a controlled set of frequent sequences. Both positional and temporal gap constraints, as well as appropriate maximality and closedness constraints, are supported. At its heart, MG-FSM partitions the input database in a way that allows us to mine each partition independently using any existing frequent sequence mining algorithm. We introduce the notion of ω-equivalency, which is a generalization of the notion of a “projected database” used by many frequent pattern mining algorithms. We also present a number of optimization techniques that minimize partition size, and therefore computational and communication costs, while still maintaining correctness. Our experimental study in the contexts of text mining and session analysis suggests that MG-FSM is significantly more efficient and scalable than alternative approaches

A unified framework for frequent sequence mining with subsequence constraints

Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this article, we show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive “pattern expressions” to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to succinct finite-state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms—although more general—are efficient and, when used for sequence mining with prior constraints studied in literature, competitive to (and in some cases superior to) state-of-the-art specialized methods

The DESQ framework for declarative and scalable frequent sequence mining

DESQ is a general-purpose framework for declarative and scalable frequent sequence mining. Applications express their speciĄc sequence mining tasks using a simple yet powerful powerful pattern expression language, and DESQŠs computation engine automatically executes the mining task in an efficient and scalable way. In this paper, we give a brief overview of DESQ and its components

Fully Parallel Inference inMarkov Logic Networks

Abstract: Markov logic is apowerful tool for handling the uncertainty that arises in real-world structured data; it has been applied successfully to anumber ofdata management problems. In practice, the resulting ground Markov logic networks can get very large, which poses challenges to scalable inference. In this paper, we present the first fully parallelized approach toinference in Markov logic networks. Inference decomposes into agrounding step and a probabilistic inference step, both of which can be cost-intensive. We propose aparallel grounding algorithm that partitions the Markov logic network based on its corresponding join graph; each partition is ground independently and in parallel. Our partitioning scheme is based on importance sampling, which we use for parallel probabilistic inference, and is also well-suited to other, more efficient parallel inference techniques. Preliminary experiments suggest that significant speedup can be gained by parallelizing both grounding and probabilistic inference.

AdCom : Adaptive combiner for streaming aggregations

Continuous applications such as device monitoring and anomaly detection often require real-time aggregated statistics over unbounded data streams. While existing stream processing systems such as Flink, Spark, and Storm support processing of streaming aggregations, their optimizations are limited with respect to the dynamic nature of the data, and therefore are suboptimal when the workload changes and/or when there is data skew. In this paper we present AdCom, which is an adaptive combiner for stream processing engines. The use of AdCom in aggregation queries enables pre-aggregating tuples upstream (i.e., before data shuffling) followed by global aggregation downstream. In contrast to existing approaches, AdCom can automatically adjust the number of tuples to pre-aggregate depending on the data rate and available network. Our experimental study using real-world streaming workloads shows that using AdCom leads to 2.5-9× higher sustainable throughput without compromising latency

Apache Wayang: A Unified Data Analytics Framework

The large variety of specialized data processing platforms and the increased complexity of data analytics has led to the need for unifying data analytics within a single framework. Such a framework should free users from the burden of (i) choosing the right platform(s) and (ii) gluing code between the different parts of their pipelines. Apache Wayang (Incubating) is the only open-source framework that provides a systematic solution to unified data analytics by integrating multiple heterogeneous data processing platforms. It achieves that by decoupling applications from the underlying platforms and providing an optimizer so that users do not have to specify the platforms on which their pipeline should run. Wayang provides a unified view and processing model, effectively integrating the hodgepodge of heterogeneous platforms into a single framework with increased usability without sacrificing performance and total cost of ownership. In this paper, we present the architecture of Wayang, describe its main components, and give an outlook on future directions