Discovering general partial orders in event streams

Frequent episode discovery is a popular framework for pattern discovery in event streams. An episode is a partially ordered set of nodes with each node associated with an event type. Efficient (and separate) algorithms exist for episode discovery when the associated partial order is total (serial episode) and trivial (parallel episode). In this paper, we propose efficient algorithms for discovering frequent episodes with general partial orders. These algorithms can be easily specialized to discover serial or parallel episodes. Also, the algorithms are flexible enough to be specialized for mining in the space of certain interesting subclasses of partial orders. We point out that there is an inherent combinatorial explosion in frequent partial order mining and most importantly, frequency alone is not a sufficient measure of interestingness. We propose a new interestingness measure for general partial order episodes and a discovery method based on this measure, for filtering out uninteresting partial orders. Simulations demonstrate the effectiveness of our algorithms

ONCE and ONCE+: Counting the Frequency of Time-constrained Serial Episodes in a Streaming Sequence

As a representative sequential pattern mining problem, counting the frequency of serial episodes from a streaming sequence has drawn continuous attention in academia due to its wide application in practice, e.g., telecommunication alarms, stock market, transaction logs, bioinformatics, etc. Although a number of serial episodes mining algorithms have been developed recently, most of them are neither stream-oriented, as they require multi-pass of dataset, nor time-aware, as they fail to take into account the time constraint of serial episodes. In this paper, we propose two novel one-pass algorithms, ONCE and ONCE+, each of which can respectively compute two popular frequencies of given episodes satisfying predefined time-constraint as signals in a stream arrives one-after-another. ONCE is only used for non-overlapped frequency where the occurrences of a serial episode in sequence are not intersected. ONCE+ is designed for the distinct frequency where the occurrences of a serial episode do not share any event. Theoretical study proves that our algorithm can correctly mine the frequency of target time constraint serial episodes in a given stream. Experimental study over both real-world and synthetic datasets demonstrates that the proposed algorithm can work, with little time and space, in signal-intensive streams where millions of signals arrive within a single second. Moreover, the algorithm has been applied in a real stream processing system, where the efficacy and efficiency of this work is tested in practical applications.Comment: 14 pages, 7 figures, 4 table

A unified view of Automata-based algorithms for Frequent Episode Discovery

Frequent Episode Discovery framework is a popular framework in Temporal Data Mining with many applications. Over the years many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper we present a unified view of all such frequency counting algorithms. We present a generic algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies and we present quantitative relationships among different frequencies. Our unified view also helps in obtaining correctness proofs for various algorithms as we show here. We also point out how this unified view helps us to consider generalization of the algorithm so that they can discover episodes with general partial orders

Discovering Compressing Serial Episodes from Event Sequences

Most pattern mining methods output a very large number of frequent patterns and isolating a small but relevant subset is a challenging problem of current interest in frequent pattern mining. In this paper we consider discovery of a small set of relevant frequent episodes from data sequences. We make use of the Minimum Description Length principle to formulate the problem of selecting a subset of episodes. Using an interesting class of serial episodes with inter-event constraints and a novel encoding scheme for data using such episodes, we present algorithms for discovering small set of episodes that achieve good data compression. Using an example of the data streams obtained from distributed sensors in a composable coupled conveyor system, we show that our method is very effective in unearthing highly relevant episodes and that our scheme also achieves good data compression.Comment: 27 pages 3 figur

Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors

Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic, perspectives into brain function. Mining event streams from these chips is critical to understanding the firing patterns of neurons and to gaining insight into the underlying cellular activity. We present a GPGPU solution to mining spike trains. We focus on mining frequent episodes which captures coordinated events across time even in the presence of intervening background/"junk" events. Our algorithmic contributions are two-fold: MapConcatenate, a new computation-to-core mapping scheme, and a two-pass elimination approach to quickly find supported episodes from a large number of candidates. Together, they help realize a real-time "chip-on-chip" solution to neuroscience data mining, where one chip (the multi-electrode array) supplies the spike train data and another (the GPGPU) mines it at a scale unachievable previously. Evaluation on both synthetic and real datasets demonstrate the potential of our approach

Inferring Neuronal Network Connectivity using Time-constrained Episodes

Discovering frequent episodes in event sequences is an interesting data mining task. In this paper, we argue that this framework is very effective for analyzing multi-neuronal spike train data. Analyzing spike train data is an important problem in neuroscience though there are no data mining approaches reported for this. Motivated by this application, we introduce different temporal constraints on the occurrences of episodes. We present algorithms for discovering frequent episodes under temporal constraints. Through simulations, we show that our method is very effective for analyzing spike train data for unearthing underlying connectivity patterns.Comment: 9 pages. See also http://neural-code.cs.vt.edu

Temporal data mining for root-cause analysis of machine faults in automotive assembly lines

Engine assembly is a complex and heavily automated distributed-control process, with large amounts of faults data logged everyday. We describe an application of temporal data mining for analyzing fault logs in an engine assembly plant. Frequent episode discovery framework is a model-free method that can be used to deduce (temporal) correlations among events from the logs in an efficient manner. In addition to being theoretically elegant and computationally efficient, frequent episodes are also easy to interpret in the form actionable recommendations. Incorporation of domain-specific information is critical to successful application of the method for analyzing fault logs in the manufacturing domain. We show how domain-specific knowledge can be incorporated using heuristic rules that act as pre-filters and post-filters to frequent episode discovery. The system described here is currently being used in one of the engine assembly plants of General Motors and is planned for adaptation in other plants. To the best of our knowledge, this paper presents the first real, large-scale application of temporal data mining in the manufacturing domain. We believe that the ideas presented in this paper can help practitioners engineer tools for analysis in other similar or related application domains as well

Efficient Discovery of Large Synchronous Events in Neural Spike Streams

We address the problem of finding patterns from multi-neuronal spike trains that give us insights into the multi-neuronal codes used in the brain and help us design better brain computer interfaces. We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication. With large electrode arrays, it is now possible to simultaneously record the spiking activity of hundreds of neurons over large periods of time. Recently, techniques have been developed to efficiently count the frequency of synchronous firing patterns. However, when the number of neurons being observed grows they suffer from the combinatorial explosion in the number of possible patterns and do not scale well. In this paper, we present a temporal data mining scheme that overcomes many of these problems. It generates a set of candidate patterns from frequent patterns of smaller size; all possible patterns are not counted. Also we count only a certain well defined subset of occurrences and this makes the process more efficient. We highlight the computational advantage that this approach offers over the existing methods through simulations. We also propose methods for assessing the statistical significance of the discovered patterns. We detect only those patterns that repeat often enough to be significant and thus be able to automatically fix the threshold for the data-mining application. Finally we discuss the usefulness of these methods for brain computer interfaces

Efficient Discovery of Large Synchronous Events in Neural Spike Streams

We address the problem of finding patterns from multi-neuronal spike trains that give us insights into the multi-neuronal codes used in the brain and help us design better brain computer interfaces. We focus on the synchronous firings of groups of neurons as these have been shown to play a major role in coding and communication. With large electrode arrays, it is now possible to simultaneously record the spiking activity of hundreds of neurons over large periods of time. Recently, techniques have been developed to efficiently count the frequency of synchronous firing patterns. However, when the number of neurons being observed grows they suffer from the combinatorial explosion in the number of possible patterns and do not scale well. In this paper, we present a temporal data mining scheme that overcomes many of these problems. It generates a set of candidate patterns from frequent patterns of smaller size; all possible patterns are not counted. Also we count only a certain well defined subset of occurrences and this makes the process more efficient. We highlight the computational advantage that this approach offers over the existing methods through simulations. We also propose methods for assessing the statistical significance of the discovered patterns. We detect only those patterns that repeat often enough to be significant and thus be able to automatically fix the threshold for the data-mining application. Finally we discuss the usefulness of these methods for brain computer interfaces

Ranking Episodes using a Partition Model

One of the biggest setbacks in traditional frequent pattern mining is that overwhelmingly many of the discovered patterns are redundant. A prototypical example of such redundancy is a freerider pattern where the pattern contains a true pattern and some additional noise events. A technique for filtering freerider patterns that has proved to be efficient in ranking itemsets is to use a partition model where a pattern is divided into two subpatterns and the observed support is compared to the expected support under the assumption that these two subpatterns occur independently. In this paper we develop a partition model for episodes, patterns discovered from sequential data. An episode is essentially a set of events, with possible restrictions on the order of events. Unlike with itemset mining, computing the expected support of an episode requires surprisingly sophisticated methods. In order to construct the model, we partition the episode into two subepisodes. We then model how likely the events in each subepisode occur close to each other. If this probability is high---which is often the case if the subepisode has a high support---then we can expect that when one event from a subepisode occurs, then the remaining events occur also close by. This approach increases the expected support of the episode, and if this increase explains the observed support, then we can deem the episode uninteresting. We demonstrate in our experiments that using the partition model can effectively and efficiently reduce the redundancy in episodes