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A survey of temporal data mining

Srivatsan Laxman, P. S. Sastry
2006 Sadhana (Bangalore)  
The field of temporal data mining is concerned with such analysis in the case of ordered data streams with temporal interdependencies.  ...  Data mining is concerned with analysing large volumes of (often unstructured) data to automatically discover interesting regularities or relationships which in turn lead to better understanding of the  ...  Just like in periodicity analysis for signal processing applications, in data mining also, there is a need to find some interesting ways to relax the periodicity constraints.  ... 
doi:10.1007/bf02719780 fatcat:nqyjuthpmreclhj4lygqyxxmk4

A Comprehensive Survey of Pattern Mining: Challenges and Opportunities

Pragati Upadhyay, M. K., Narendra Kohli
2018 International Journal of Computer Applications  
In this regard periodic pattern mining was introduced. It finds out the patterns that appear periodically and frequently in the database. PPM [56] and Twain [57] proposed this periodic pattern.  ...  Sequential pattern mining is capable to discover in a sequential database.  ... 
doi:10.5120/ijca2018916573 fatcat:dktrnmfvhzadzm5rhutwijfkvy

Discovering similar patterns in time series

Juan P. Caraça-Valente, Ignacio López-Chavarrías
2000 Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00  
(i) Each recurring pattern is associated with temporal information pertaining to its durations of periodic appearances in a series.  ...  Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series.  ...  Thus, it is necessary to relax this strict constraint without changing the period threshold value.  ... 
doi:10.1145/347090.347192 dblp:conf/kdd/Caraca-ValenteL00 fatcat:2m6xmosxefgvbigb3mybgocquu

Looking into the seeds of time: Discovering temporal patterns in large transaction sets

2006 Information Sciences  
Intuitively, these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p, but not at time points not in p.  ...  A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent.  ...  Frequent Itemsets and Their Temporal Patterns Let database D be a set of transactions. Each transaction is a structure composed by an itemset in the itemset space and a time point in the time period.  ... 
doi:10.1016/j.ins.2005.01.019 fatcat:sjuyfgeg45emdme2m4mvxyhil4

A Regression-Based Temporal Pattern Mining Scheme for Data Streams [chapter]

Wei-Guang Teng, Ming-Syan Chen, Philip S. Yu
2003 Proceedings 2003 VLDB Conference  
We devise in this paper a regression-based algorithm, called algorithm FTP-DS (Frequent Temporal Patterns of Data Streams), to mine frequent temporal patterns for data streams.  ...  In addition, we develop the techniques of the segmentation tuning and segment relaxation to enhance the functions of FTP-DS.  ...  Acknowledgement The authors are supported in part by the National Science Council, Project No. NSC 91-2213-E-002-034 and NSC 91-2213-E-002-045, Taiwan, Republic of China.  ... 
doi:10.1016/b978-012722442-8/50017-3 dblp:conf/vldb/TengCY03 fatcat:vhafzvjx3ra7lgtkfzlrt5t5di

Mining Periodic Patterns from Non-binary Transactions

Jhimli Adhikari
2018 Journal of Intelligent Computing  
In first phase we mined locally frequent item sets along with the set of intervals and their database frequency range and second phase mines the two types of periodic patterns (cyclic and acyclic) from  ...  To solve this problem, we incorporate the concept of transaction frequency (TF) and database frequency (DF) of an item in a time interval. Our algorithm works in two phases.  ...  Periodicity detection is a process for finding temporal regularities within the time-stamped database. is associated with temporal information pertaining to its durations of periodic appearances in a database  ... 
doi:10.6025/jic/2018/9/4/144-156 fatcat:b3kafkyvjrbd5htwj2t54g55rm

Social Search and Querying [chapter]

Georgia Koloniari, Panagiotis Lionakis, Kostas Stefanidis
2017 Encyclopedia of Social Network Analysis and Mining  
In this position paper, to increase the effectiveness of social search queries, we propose exploiting the temporal information available in social networks.  ...  In particular, we introduce different types of queries aiming at satisfying information needs from different perspectives.  ...  We are inspired by a traditional temporal database that includes temporal aspects, such as the valid and transaction time of data items [10] .  ... 
doi:10.1007/978-1-4614-7163-9_110208-1 fatcat:cmp235zqgbf3tbqd7x57gqgsu4

A Survey on Destination Prediction Using Trajectory Data Mining Technique

Banupriya C S
2016 International Journal Of Engineering And Computer Science  
Mobility pattern of the user is predicted using next check-in data. Prediction features that exploit different information dimensions about users based on venue prediction.  ...  INTRODUCTION Data mining is the process which is discovering pattern in large set of data involves application of artificial intelligence, machine learning, statistics and database system.  ...  Figure 2 2 Figure 2 a) Trajectories in real world b) Trajectories stored in database  ... 
doi:10.18535/ijecs/v5i12.67 fatcat:lzg2mq6fdnfstj3ll4s76a35je

A Visual Interface for Multivariate Temporal Data: Finding Patterns of Events across Multiple Histories

Jerry Fails, Amy Karlson, Layla Shahamat, Ben Shneiderman
2006 2006 IEEE Symposium On Visual Analytics And Technology  
Finding patterns of events over time is important in searching patient histories, web logs, news stories, and criminal activities.  ...  We define temporal patterns as sequences of events with interevent time spans.  ...  Databases Over the years databases have progressed from theory, to small text files, to visual representations, to presently include research in spatial-temporal databases.  ... 
doi:10.1109/vast.2006.261421 dblp:conf/ieeevast/FailsKSS06 fatcat:pngapxnglrfqdllpo5pwluucym


Zhenhui Li, Ming Ji, Jae-Gil Lee, Lu-An Tang, Yintao Yu, Jiawei Han, Roland Kays
2010 Proceedings of the 2010 international conference on Management of data - SIGMOD '10  
., in ecological study, vehicle control, mobile communication management, and climatological forecast.  ...  At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.  ...  Approximation on both spatial and temporal dimensions is essential to mine the hidden periodic patterns.  ... 
doi:10.1145/1807167.1807319 dblp:conf/sigmod/LiJLTYHK10 fatcat:x3676qs5ijgqnjjxwkvokuef3y

Skew-aware automatic database partitioning in shared-nothing, parallel OLTP systems

Andrew Pavlo, Carlo Curino, Stanley Zdonik
2012 Proceedings of the 2012 international conference on Management of Data - SIGMOD '12  
transactions, while concurrently mitigating the effects of temporal skew in both the data distribution and accesses, (2) extending the design space to include replicated secondary indexes, (4) organically  ...  Deriving such designs for modern DBMSs is difficult, especially for enterprise-class OLTP systems, since they impose extra challenges: the use of stored procedures, the need for load balancing in the presence  ...  Relaxation Relaxation is the process of selecting random tables in the database and resetting their chosen partitioning attributes in the current best design.  ... 
doi:10.1145/2213836.2213844 dblp:conf/sigmod/PavloCZ12 fatcat:b4ia3aszjfgzbf4ebgyxfo5guq


Sandra de Amo, Waldecir P. Junior, Arnaud Giacometti
2008 International Journal of Data Warehousing and Mining  
In this paper, we consider a new kind of temporal pattern where both interval and punctual time representation are considered.  ...  The datasets where these kind of patterns may appear are temporal relational databases whose relations contain point or interval timestamps.  ...  For instance, (1) in a medical application, we could be interested in discovering if patients who take some medicine X during a certain period of time P , and in some moment m in P undergo a stomach surgery  ... 
doi:10.4018/jdwm.2008100103 fatcat:gspbj3bkvrblxais727hf4pjci

Contrast set mining in temporal databases

André Magalhães, Paulo J. Azevedo
2014 Expert systems  
We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line.  ...  A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task.  ...  In area 6, the antecedent relaxation feature is present. It attempts to help the user in finding a possible explanation to why some specific contrast was not discovered in a specific period.  ... 
doi:10.1111/exsy.12080 fatcat:p7focfwt3rejrmhdhpo3y7jmxu

Temporal fuzzy association rule mining with 2-tuple linguistic representation

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood, Samad Ahmadi
2012 2012 IEEE International Conference on Fuzzy Systems  
The novel application of the 2tuple linguistic representation identifies fuzzy association rules in a temporal context, whilst maintaining the interpretability of linguistic terms.  ...  This paper reports on an approach that contributes towards the problem of discovering fuzzy association rules that exhibit a temporal pattern.  ...  Partially periodic rules [29] relax the regularity found in fully periodic rules so the cyclic behaviour is found in only some segments of the dataset and is not always repeated regularly.  ... 
doi:10.1109/fuzz-ieee.2012.6251173 dblp:conf/fuzzIEEE/MatthewsGHA12 fatcat:5bn3q2wj3jcinoehl6swdparhi

Temporal Data Mining Using Hidden Markov-Local Polynomial Models [chapter]

Weiqiang Lin, Mehmet A. Orgun, Graham J. Williams
2001 Lecture Notes in Computer Science  
In our method, there are three levels for mining similarity and periodicity patterns.  ...  This study proposes a data mining framework to discover qualitative and quantitative patterns in discrete-valued time series (DTS).  ...  Introduction Temporal data mining is concerned with discovering qualitative and quantitative temporal patterns in a temporal database or in a discrete-valued time series (DTS) dataset.  ... 
doi:10.1007/3-540-45357-1_35 fatcat:drfuyz477ngndheal4xg3hyhei
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