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Work-efficient Batch-incremental Minimum Spanning Trees with Applications to the Sliding Window Model
[article]
2020
arXiv
pre-print
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Using our batch-incremental MST algorithm, we demonstrate a range of applications that become efficiently solvable in parallel in the sliding-window model, such as graph connectivity, approximate MSTs, ...
In this paper, we present the first work-efficient parallel batch-dynamic algorithm for incremental MST, which can insert ℓ edges in O(ℓlog(1+n/ℓ)) work in expectation and O(polylog(n)) span w.h.p. ...
Acknowledgements This work was supported in part by NSF grants CCF-1408940 and CCF-1629444. The authors would like to thank Ticha Sethapakdi for helping with the figures. ...
arXiv:2002.05710v1
fatcat:fqp6urbg5fcaxaajdsapoysr4q
Slider
2014
Proceedings of the 15th International Middleware Conference on - Middleware '14
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When pairs of consecutive windows overlap, there is a potential to update the output incrementally, more efficiently than recomputing from scratch. ...
However, in most systems, realizing this potential requires programmers to explicitly manage the intermediate state for overlapping windows, and devise an application-specific algorithm to incrementally ...
Acknowledgements We are thankful to Paarijaat Aditya, Marcel Dischinger, and Farshad Kooti for helping us with the evaluation of the case studies. ...
doi:10.1145/2663165.2663334
dblp:conf/middleware/BhatotiaAJR14
fatcat:td5fiak5yjhrhcl4gu6awsz7v4
A Review: Frequent Pattern Mining Techniques in Static and Stream Data Environment
2016
Indian Journal of Science and Technology
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The problems like concept drifting, nature of data, its processing model, inadequacy, size, its retention in warehouses etc. are also required to be taken into account while working with real time data ...
A confidence defines if a person purchases any item M, how many times he purchases item N with it 1 . Minimum confidence value is set to validate associations. ...
The algorithm uses sliding window model to mine the most recent data. The model works by putting transactions into batches. A sliding window is composed of a number of basic windows. ...
doi:10.17485/ijst/2016/v9i45/106350
fatcat:v2qym4qpcndfbgyzb3rmx35axq
Regular Path Query Evaluation on Streaming Graphs
[article]
2020
arXiv
pre-print
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We adopt the Regular Path Query (RPQ) model that specifies navigational patterns with labeled constraints. ...
Experimental analysis on real and synthetic streaming graphs shows that the proposed algorithms can process up to tens of thousands of edges per second and efficiently answer RPQs that are commonly used ...
The proposed algorithms incrementally maintain query answers as the window slides thus eliminating the computational overhead of the naive strategy of batch computation after each window movement. ...
arXiv:2004.02012v1
fatcat:733z3i5w6bcb5dx6rbhi2nd6eu
GASSER: An Auto-Tunable System for General Sliding-Window Streaming Operators on GPUs
2019
IEEE Access
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INDEX TERMS Data stream processing, sliding-window queries, GPU processing, autotuning, selfconfiguring systems. ...
Furthermore, Gasser provides an auto-tuning approach able to automatically find the optimal value of the configuration parameters (i.e., batch length and the degree of parallelism) needed to optimize throughput ...
However, the system does not support sliding window since windows span across several batches. ...
doi:10.1109/access.2019.2910312
fatcat:l377xhz3ybg4ray3qbwt47teii
Efficient anomaly monitoring over moving object trajectory streams
2009
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09
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Then, we utilize the local continuity characteristics of trajectories to build local clusters upon trajectory streams and monitor anomalies via efficient pruning strategies. ...
Our extensive experiments demonstrate the effectiveness and efficiency of our methods. ...
by v VP-trees, from VP-tree 1 to Vp-tree v, except some boundary ones near the start or end of the sliding window. ...
doi:10.1145/1557019.1557043
dblp:conf/kdd/BuCFL09
fatcat:zp4jfgxpujdwfof5psysqyfjd4
Memory-adaptive high utility sequential pattern mining over data streams
2017
Machine Learning
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Furthermore, in order to show the effectiveness and efficiency of MAHUSP in real-life applications, we apply our proposed algorithm to a web clickstream dataset obtained from a Canadian news portal to ...
Our experimental study shows that our algorithm can not only discover HUSPs over data streams efficiently, but also adapt to memory allocation with limited sacrifices in the quality of discovered HUSPs ...
The sliding window model captures a fixed number of most recent records (e.g., instances/batches of instances) in a window, and it focuses on discovering the patterns within the window. ...
doi:10.1007/s10994-016-5617-1
fatcat:vymcebqsq5bv7fyaa6lfwsxsae
Incremental Bayesian network structure learning in high dimensional domains
2013
2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)
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There are few works addressing this issue and they differs with respect to the way of dealing with incrementality. Some works consider sliding windows, i.e. ...
For this reason, incremental learning algorithms are needed in order to efficiently integrate the novel data with the existing knowledge. ...
doi:10.1109/icmsao.2013.6552635
fatcat:5bvt6lavgjdlnpfetaybqty44q
Incremental mining of sequential patterns: Progress and challenges
2013
Intelligent Data Analysis
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Sequential pattern mining is a vital problem with broad applications. ...
The three more relevant algorithms, based on this approach, are also studied in depth along with the other work done in this area. This would give scope for future research direction. ...
ICspan algorithm, hence proposed the use of sliding window model that sample the data from the input stream into units. ...
doi:10.3233/ida-130591
fatcat:ituubtk5ujca3bxmrxyl44zgdm
Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis
2014
International Journal of Distributed Sensor Networks
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Classification algorithms in traditional data mining approach offer fair accuracy by training a classification model with the full dataset, in batches. ...
Since sonar signal data streams can amount to infinity, the data preprocessing time must be kept to a minimum to fulfill the need for high speed. ...
Acknowledgments The authors are thankful for the financial support from the Research Grant "Adaptive OVFDT with Incremental Pruning and ROC Corrective Learning for Data Stream Mining, " Grant no. ...
doi:10.1155/2014/635834
fatcat:f5yrdjo2wfhi5hsmqenwomr2wi
Efficient Incremental Computation of Aggregations over Sliding Windows
2021
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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compute sliding window aggregations efficiently. ...
PBA runs in 𝑂 (1) time, performing at most 3 merging operations per slide while consuming 𝑂 (𝑛) space for windows with 𝑛 partial aggregations. ...
ACKNOWLEDGMENTS This research was financed by the French government IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25). ...
doi:10.1145/3447548.3467360
fatcat:szdsgp2wvrgerctgbiytdhq4du
Real-Time Detection of In-flight Aircraft Damage
2017
Journal of Classification
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Random Forests provide a computationally efficient approach with sufficient accuracy to be able to detect and classify the different scenarios in real-time. ...
We demonstrate our approach by classifying realizations of a 45 degree bank angle generated from the Generic Transport Model flight simulator in collaboration with NASA. ...
Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement by the United States Government ...
doi:10.1007/s00357-017-9237-7
fatcat:fvcmeyz5x5dmzctkilfqe2agqq
SABER
2016
Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16
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It must do this while respecting the semantics of streaming SQL queries, in particular with regard to window handling. ...
Such heterogeneous architectures have the potential to improve the performance of data-intensive stream processing applications, but they are not supported by current relational stream processing engines ...
For sliding windows, tasks perform incremental computation on the windows in a batch to avoid redundant computation. ...
doi:10.1145/2882903.2882906
dblp:conf/sigmod/KoliousisWFWCP16
fatcat:x5oopplx3zepto4zttbwspbl6a
Continuous Prediction of Closed Frequent Itemsets from High speed Distributed Data Streams using Parallel Mining on Manifold Windows with Varying Size
2014
International Journal of Computer Applications
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Continuous prediction of closed frequent itemsets from high speed distributed data streams is an active research work, which is because of the conflict to the process time taken to perform mining consistent ...
The said model is referred as Parallel Closed Frequent Itemsets Mining (PCFIM) over High Speed Distributed Data streams by Manifold Varying Size Windows (MVSW). ...
ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers for their valuable comments. We also thank the authors of all references for helping us setup the paper. ...
doi:10.5120/17662-8479
fatcat:ykuilwrrpfbqbipdnivignntx4
Dynamic diversification of continuous data
2012
Proceedings of the 15th International Conference on Extending Database Technology - EDBT '12
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The diversification problem is in general NP-complete; we provide theoretical bounds that characterize the quality of our solution based on cover trees with respect to the optimal solution. ...
Finally, we report experimental results concerning the efficiency and effectiveness of our approach on a variety of real and synthetic datasets. ...
We would like to thank the authors of [23] for providing us with their version of the "Faces" dataset. ...
doi:10.1145/2247596.2247623
dblp:conf/edbt/DrosouP12
fatcat:ddcexve755c4ndowhyrdtyeod4
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