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Acquiring Business Intelligence through Temporal Mining of Smart Meter Data

Siva Koteswara Rao Chinnam, AV Krishna Prasad, B. Premamayudu, Moka Vinod, Hye-Jin Kim
2016 International Journal of Software Engineering and Its Applications  
Now by applying temporal mining techniques on this smart meter data we attempt to show how the Business intelligence can be improved by data analysis and analytics.  ...  This paper is about using efficient mining techniques on real time smart meter data for any utility like water, power or gas etc.  ...  In utility computing data acquisition clustering can be based on every temporal data type like event based, time stamp based, real time volumes.  ... 
doi:10.14257/ijseia.2016.10.9.12 fatcat:jjf4ki64fnffdmmvvkmyuk67ne

Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms

Wei Fang, Yupeng Chen, Qiongying Xue
2021 Journal on Big Data  
in processing Spatio-temporal sequence data.  ...  This paper reviews the RNN-based Spatiotemporal sequence prediction algorithm, introduces the development history of RNN and the common application directions of the Spatio-temporal sequence prediction  ...  For example, the method of sliding time window is used to extract data from the original data at intermediate intervals (such as one day, one week, etc.) to form a new Spatio-temporal sequence [33] ,  ... 
doi:10.32604/jbd.2021.016993 fatcat:tu5ctgr5p5em7afjc66wqwq3ya

A study on sequential pattern mining on chemical information

S Sathya, N Rajendran
2018 International Journal of Engineering & Technology  
However, large-scale sequential data is a fundamental problem like higher classification time and bonding time in data mining with many applications.  ...  Data mining determines explanation through clustering visualization, association and sequential analysis. Chemical compounds are well-defined structures compressed by a graph representation.  ...  Temporal skeletonization on sequential data Sequential pattern analysis aims on locating thesignificant temporal structures where the values are distributed in a series.  ... 
doi:10.14419/ijet.v7i2.33.14828 fatcat:4xofoypxcnhodolqoywc256mqe

A Sequence Data Model for Analysing Temporal Patterns of Student Data

Mohammad Javad Mahzoon, Mary Lou Maher, Omar Eltayeby, Wenwen Dou, Kazjon Grace
2018 Journal of Learning Analytics  
We present a model based on temporal relationships of heterogeneous data as the basis for building predictive models.  ...  Our results for the two sequence models show that analytics based on the sequence data model can achieve higher predictive accuracy than non-temporal models with the same data.  ...  the within-semester sequence model analysis.  ... 
doi:10.18608/jla.2018.51.5 fatcat:slj6maoji5civakkvplzlwmox4

Learning Predictive Models from Integrated Healthcare Data: Extending Pattern-Based and Generative Models to Capture Temporal and Cross-Attribute Dependencies

Rui Henriques, Claudia Antunes
2014 2014 47th Hawaii International Conference on System Sciences  
However, existing predictive models are not yet able to successfully anticipate health conditions based on multiple (sparse) time sequences derived from repositories of health-records.  ...  To tackle this problem, we propose new predictive models able to learn from an expressive temporal structure, a time-enriched itemset sequence, which captures both temporal and cross-attribute dependencies  ...  To be able to learn HMMs from (time-enriched) itemset sequences, we propose a new classifier, referred as M2ID (Markov-based Models from Integrated Data), that relies on a simple data mapping applied over  ... 
doi:10.1109/hicss.2014.322 dblp:conf/hicss/HenriquesA14 fatcat:ucij5qbq6zdidmzll7q5wvtp4y

Interval-Based Linear Hybrid Dynamical System for Modeling Cross-Media Timing Structures in Multimedia Signals

Hiroaki Kawashima, Takashi Matsuyama
2007 14th International Conference on Image Analysis and Processing (ICIAP 2007)  
We then propose a cross-media timingstructure model to represent dynamic structures among multiple media signals based on the relation of temporal intervals described by multiple ILHDSs.  ...  In this paper, we propose a computational scheme named an interval-based linear hybrid dynamical system (ILHDS) to represent complex dynamic events based on temporal intervals, each of which is characterized  ...  An interval sequence I = {I 1 , ..., I K } is generated from I ′ based on the cross-media timing-structure model.  ... 
doi:10.1109/iciap.2007.4362872 dblp:conf/iciap/KawashimaM07 fatcat:nuanntf4areida3sqqcgxke7lu

3D Spatio-Temporal face recognition using dynamic range model sequences

Yi Sun, Lijun Yin
2008 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops  
Based on our newly created 3D dynamic face database, we propose to use a Spatio-Temporal Hidden Markov Model (HMM) which incorporates 3D surface feature characterization to learn the spatial and temporal  ...  The advantage of using 3D dynamic data for face recognition has been evaluated by comparing our approach to three conventional approaches: 2D video based temporal HMM model, conventional 2Dtexture based  ...  Temporal HMM (T-HMM) To explore the temporal dynamics of the 3D facial surface along a time sequence, the temporal HMM takes each frame of a face sequence as one observation.  ... 
doi:10.1109/cvprw.2008.4563125 dblp:conf/cvpr/SunY08 fatcat:b6xywriuxfeh5lijqq3k7k4tqe

Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection [article]

Heejeong Choi, Subin Kim, Pilsung Kang
2022 arXiv   pre-print
It enables productivity improvement and maintenance cost reduction by preventing malfunctions and detecting anomalies based on time-series data.  ...  However, multivariate time-series anomaly detection is challenging because real-world time-series data exhibit complex temporal dependencies.  ...  In [28] , a model based on a CNN that generates a future frame from an input video sequence was proposed.  ... 
arXiv:2202.10001v1 fatcat:mydjcix23nha5eomol652nlmfm

Indexing and retrieval of video based on spatial relation sequences

Serhan Dağtaş, Arif Ghafoor
1999 Proceedings of the seventh ACM international conference on Multimedia (Part 2) - MULTIMEDIA '99  
An important aspect of video data is its spatio-temporal aemantics.  ...  This is due to the dependency of the complex content descriptions on the apatio-temporal features.  ...  Acknowledgement This work has been partially funded by a National Science Foundation grant under grant number IRI-9619812.  ... 
doi:10.1145/319878.319910 dblp:conf/mm/DagtasG99 fatcat:fcknrcn46bgajap7lpbqqm7oje

The Real-Time and Patient-Specific Prediction for Duration and Recovery Profile of Cisatracurium Based on Deep Learning Models

Kan Wang, Binyu Gao, Heqi Liu, Hui Chen, Honglei Liu
2022 Frontiers in Pharmacology  
TOFR of cisatracurium could be regarded as temporal sequence data, which could be processed and predicted using RNN based deep learning methods.  ...  In transfer learning, the model chosen based on patient similarity has significantly outperformed the model chosen randomly.  ...  Therefore, compared with RNN, LSTM models are more suited to processing and making predictions based on temporal data.  ... 
doi:10.3389/fphar.2021.831149 pmid:35185552 pmcid:PMC8854501 fatcat:sddfy32gsjbvplbkskl5zu4sii

Short-term recognition memory for serial order and timing

Simon Farrell, Karis McLaughlin
2007 Memory & Cognition  
Recent evidence suggests that a common temporal representation underlies memory for serial order of items in a sequence, and the timing of items in a sequence.  ...  Application of a temporal matching model of serial and temporal recognition suggests that although participants were able to remember the timing of items, this memory for timing was unlikely to determine  ...  The temporal recognition task provides useful data on short-term memory for time; since time-based models assume that participants memory for the time of events is used to determine their order, a necessary  ... 
doi:10.3758/bf03193505 pmid:18062549 fatcat:ghdczkbegzbcxl3nw2fwqc5sly

Structuring ordered nominal data for event sequence discovery

Chreston A. Miller, Francis Quek, Naren Ramakrishnan
2010 Proceedings of the international conference on Multimedia - MM '10  
The procedure is discussed along with results computed using a representative data set characterized by nominal event data.  ...  Given a parsed sequence of events, relational information pertinent to comparison between events can be obtained through the application of n-grams techniques borrowed from speech processing and temporal  ...  One possible answer is by probabilistic modeling of the temporal sequence of events. We base our temporal relationship model on Allen's temporal relation principles [1] .  ... 
doi:10.1145/1873951.1874153 dblp:conf/mm/MillerQR10 fatcat:whu2byxxlrhltkenh4j4zspzn4

Capturing Telic/Atelic Temporal Data Semantics: Generalizing Conventional Conceptual Models

Vijay Khatri, Sudha Ram, Richard T. Snodgrass, Paolo Terenziani
2014 IEEE Transactions on Knowledge and Data Engineering  
To differentiate between telic and atelic data semantics in conceptual database design, we propose an annotation-based temporal conceptual model that generalizes the semantics of a conventional conceptual  ...  that help capture "when" semantics; 3) specifying temporal constraints, specifically non-sequenced semantics, in the temporal data dictionary as metadata.  ...  ACKNOWLEDGMENTS This work was supported in part by the National Science Foundation under grants IIS-0803229, IIS-0415101, IIS-0455993, IIS-1016205, and EIA-0080123 and by partial support from a grant from  ... 
doi:10.1109/tkde.2012.74 fatcat:hq766qz3mbh2zly2cyc6nvqmeq

Literature survey of temporal data models

2017 International Journal of Latest Trends in Engineering and Technology  
This model required coalescing. Sarda [24] designed a temporal data model incorporating HSQL, which is a calculus based valid-time query language.  ...  In this section,we have reviewed these models,basis on valid time or transaction time and then bi-temporal data models. A.  ... 
doi:10.21172/1.841.47 fatcat:odbedlm2zrabrkqf3xnjroei5q

Mining Hierarchical Temporal Patterns in Multivariate Time Series [chapter]

Fabian Mörchen, Alfred Ultsch
2004 Lecture Notes in Computer Science  
The Temporal Data Mining Method is the accompanying framework to discover temporal knowledge based on this rule language.  ...  The Unification-based Temporal Grammar is a temporal extension of static unification-based grammars.  ...  Our rule language, called Unification-based Temporal Grammar (UTG), is based on multiple data models. The problem is decomposed into the mining of single temporal concepts.  ... 
doi:10.1007/978-3-540-30221-6_11 fatcat:onmic2vrmzaoheckbduvfcm5fm
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