Mining and Forecasting of Big Time-series Data

Sakurai, Yasushi

doi:10.1145/2723372.2731081

Cited by 34 publications

(7 citation statements)

References 58 publications

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“…Computing the data having higher-level weights can obtain approximate solutions [25]. Some other data mining uses time series the determine the weights of the data by predictions [26]. However, this approach highly depends on the fault tolerance rate.…”

Section: Multimedia Big Datamentioning

confidence: 99%

Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing

Gai

Qiu

Zhao

et al. 2020

IEEE Trans. Cloud Comput.

144

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Recent expansions of Internet-of-Things (IoT) applying cloud computing have been growing at a phenomenal rate. As one of the developments, heterogeneous cloud computing has enabled a variety of cloud-based infrastructure solutions, such as multimedia big data. Numerous prior researches have explored the optimizations of on-premise heterogeneous memories. However, the heterogeneous cloud memories are facing constraints due to the performance limitations and cost concerns caused by the hardware distributions and manipulative mechanisms. Assigning data tasks to distributed memories with various capacities is a combinatorial NP-hard problem. This paper focuses on this issue and proposes a novel approach, Cost-Aware Heterogeneous Cloud Memory Model (CAHCM), aiming to provision a high performance cloud-based heterogeneous memory service offerings. The main algorithm supporting CAHCM is Dynamic Data Allocation Advance (2DA) Algorithm that uses genetic programming to determine the data allocations on the cloud-based memories. In our proposed approach, we consider a set of crucial factors impacting the performance of the cloud memories, such as communication costs, data move operating costs, energy performance, and time constraints. Finally, we implement experimental evaluations to examine our proposed model. The experimental results have shown that our approach is adoptable and feasible for being a cost-aware cloud-based solution.

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Section: Multimedia Big Datamentioning

confidence: 99%

Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing

Gai

Qiu

Zhao

et al. 2020

IEEE Trans. Cloud Comput.

144

View full text Add to dashboard Cite

show abstract

“…Pattern discovery in time series. In recent years, there has been an explosion of interest in mining time-stamped data [4,36,29,28]. Similarity search and pattern discovery in time sequences have attracted huge interest [38,26,2,37,35,29,24,5].…”

Section: Related Workmentioning

confidence: 99%

Non-Linear Mining of Competing Local Activities

Matsubara

Sakurai

Faloutsos

2016

Proceedings of the 25th International Conference on World Wide Web

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Given a large collection of time-evolving activities, such as Google search queries, which consist of d keywords/activities for m locations of duration n, how can we analyze temporal patterns and relationships among all these activities and find location-specific trends? How do we go about capturing non-linear evolutions of local activities and forecasting future patterns? For example, assume that we have the online search volume for multiple keywords, e.g., "Nokia/Nexus/Kindle" or "CNN/BBC" for 236 countries/territories, from 2004 to 2015. Our goal is to analyze a large collection of multi-evolving activities, and specifically, to answer the following questions: (a) Is there any sign of interaction/competition between two different keywords? If so, who competes with whom? (b) In which country is the competition strong? (c) Are there any seasonal/annual activities? (d) How can we automatically detect important worldwide (or local) events? We present COMPCUBE, a unifying non-linear model, which provides a compact and powerful representation of co-evolving activities; and also a novel fitting algorithm, COMPCUBE-FIT, which is parameter-free and scalable. Our method captures the following important patterns: (B)asic trends, i.e., non-linear dynamics of coevolving activities, signs of (C)ompetition and latent interaction, e.g., Nokia vs. Nexus, (S)easonality, e.g., a Christmas spike for iPod in the U.S. and Europe, and (D)eltas, e.g., unrepeated local events such as the U.S. election in 2008. Thanks to its concise but effective summarization, COMPCUBE can also forecast long-range future activities. Extensive experiments on real datasets demonstrate that COMPCUBE consistently outperforms the best state-ofthe-art methods in terms of both accuracy and execution speed.

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“…Pattern Discovery in Time Series. In recent years, there has been a huge interest in mining time-stamped data [9], [18], [21]. Traditional approaches typically use linear methods, such as autoregression (AR), linear dynamical systems (LDS), TBATS [8] and their variants [3], [5], [6], [20].…”

Section: Related Workmentioning

confidence: 99%

Non-linear Time-series Analysis of Social Influence

Minh

Matsubara

Sakurai

2016

Journal of Information Processing

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Given a large collection of time-evolving online user activities, such as Google Search queries for multiple keywords of various categories (celebrities, events, diseases, etc...), which consist of d keywords/activities, for l countries/locations of duration n, how can we find patterns and rules? For example, assume that we have the online search volume for "Harry Potter", "Barack Obama" and "Amazon", for 232 countries/territories, from 2004 to 2015, which include external shocks, sudden change of search volume, and more. How do we go about capturing non-linear evolutions of local activities and forecasting future patterns? Our goal is to analyze a large collection of time-evolving sequences, and moreover, to find the answer for the following issues: (a) Are there any important external shocks/events relating to the keywords in the sequences? (b) If there are, can we automatically detect them? (c) Are there any countries/territories which have different reacts to the global trend? In this paper, we present Δ-SPOT, a unifying analytical non-linear model for large scale web search data; as well as an efficient and effective fitting algorithm, which solves the above problems. Δ-SPOT can also forecast long-range future dynamics of the keywords/queries. Extensive experiments on real data show that our method outperforms other effective methods of non-linear mining in terms of accuracy in both fitting and forecasting.

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Mining and Forecasting of Big Time-series Data

Cited by 34 publications

References 58 publications

Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing

Cost-Aware Multimedia Data Allocation for Heterogeneous Memory Using Genetic Algorithm in Cloud Computing

Non-Linear Mining of Competing Local Activities

Non-linear Time-series Analysis of Social Influence

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