Fast Causal Network Inference over Event Streams

Acharya, Saurav; Lee, Byung Suk

doi:10.1007/978-3-642-40131-2_19

Cited by 3 publications

(8 citation statements)

References 9 publications

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“…This approach does not have the problem of equivalence classes, but performs a prohibitively large number of conditional independent tests. The state-of-the-art FCNI by the authors of this paper [12] provided a faster algorithm for constructing a traditional constraint-based causal network over event streams. (Thus, we consider the FCNI algorithm as the representative of the traditional causal network approach in this paper.)…”

Section: Related Workmentioning

confidence: 99%

“…We use a window, called partitioned window [12], to collect the events from the stream for a user-specified observation period T. To group related events in the window, these events are partitioned by the CRA and then arranged in the temporal order within individual partitions. Figure 2 shows a partitioned window for the event stream described in Fig.…”

Section: Event Streamsmentioning

confidence: 99%

“…Computational complexity of the RSET algorithm is dominated by the number of CI tests, as is for the ES algorithm, and for both algorithms the number is exponential in the worst case [12]. Hence, in the worst case both algorithms have exponential computational complexity.…”

Section: Algorithmmentioning

confidence: 99%

“…The first objective is to compare the run-time causal inference mechanism of the proposed algorithms (i.e. ES, RSET) against the state-of-the-art traditional causal inference mechanism called the Fast Causal Network Inference (FCNI) algorithm [12]. The FCNI algorithm is essentially inapplicable to our problem due to its inability to handle cyclic causality and runtime causal inference, but is the best available in the state of the art.…”

Section: Introductionmentioning

confidence: 99%

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Causal Prediction of Top-k Event Types Over Real-Time Event Streams

Acharya

Lee

Hines

2017

The Computer Journal

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This paper addresses the problem of causally predicting the top-k most likely next events over real-time event streams. Existing approaches have limitations-(i) they model causality in an acyclic causal network structure and search it to find the top-k next events, which does not work with real world event streams as they frequently manifest cyclic causality, and (ii) they prune out possible non-causal links from a causal network too aggressively and end up omitting many less frequent yet important causal links. We overcome these limitations using a novel event precedence model (EPM) and a run-time causal inference mechanism. The EPM constructs a Markov chain incrementally over event streams, where an edge between two events signifies a temporal precedence relationship between them, which is a necessary condition for causality. Then, the run-time causal inference mechanism performs causality tests on the EPM during query processing, and temporal precedence relationships that fail the causality test in the presence of other events are removed. Two query processing algorithms are presented. One performs exhaustive search on the model and the other performs more efficient reduced search with early termination. Experiments using two real data sets (cascading blackouts in power systems and web page views) verify efficacy and efficiency of the proposed probabilistic top-k prediction algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Event Streamsmentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Causal Prediction of Top-k Event Types Over Real-Time Event Streams

Acharya

Lee

Hines

2017

The Computer Journal

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“…In each evaluation, there are two objectives. The first objective is to compare the run-time causal inference mechanism of the proposed algorithms (i.e., ES, RSET) against the stateof-the-art traditional causal inference mechanism called the Fast Causal Network Inference (FCNI) algorithm [1]. The FCNI algorithm is essentially inapplicable to our problem due to its lack of ability to handle cyclic causality and run-time causal inference, but is the best available in the state of the art.…”

Section: Introductionmentioning

confidence: 99%

Real-time Top-K Predictive Query Processing over Event Streams

Acharya¹,

Lee²,

Hines³

2015

Preprint

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This paper addresses the problem of predicting the k events that are most likely to occur next, over historical real-time event streams. Existing approaches to causal prediction queries have a number of limitations. First, they exhaustively search over an acyclic causal network to find the most likely k effect events; however, data from real event streams frequently reflect cyclic causality. Second, they contain conservative assumptions intended to exclude all possible non-causal links in the causal network; it leads to the omission of many less-frequent but important causal links. We overcome these limitations by proposing a novel event precedence model and a runtime causal inference mechanism. The event precedence model constructs a first order absorbing Markov chain incrementally over event streams, where an edge between two events signifies a temporal precedence relationship between them, which is a necessary condition for causality. Then, the run-time causal inference mechanism learns causal relationships dynamically during query processing. This is done by removing some of the temporal precedence relationships that do not exhibit causality in the presence of other events in the event precedence model. This paper presents two query processing algorithms -one performs exhaustive search on the model and the other performs a more efficient reduced search with early termination. Experiments using two real datasets (cascading blackouts in power systems and web page views) verify the effectiveness of the probabilistic top-k prediction queries and the efficiency of the algorithms. Specifically, the reduced search algorithm reduced runtime, relative to exhaustive search, by 25 − 80% (depending on the application) with only a small reduction in accuracy.

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Enhanced Fast Causal Network Inference over Event Streams

Acharya

Lee

2015

Transactions on Large-Scale Data- And Knowledge-Centered Systems XVII

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Fast Causal Network Inference over Event Streams

Cited by 3 publications

References 9 publications

Causal Prediction of Top-k Event Types Over Real-Time Event Streams

Causal Prediction of Top-k Event Types Over Real-Time Event Streams

Real-time Top-K Predictive Query Processing over Event Streams

Enhanced Fast Causal Network Inference over Event Streams

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