scholarly journals Mining Weighted Periodic Patterns by a Weighted Direction Graph Based Approach for Time-Series Databases

2021 ◽  
pp. 267-284
Author(s):  
Ye-In Chang ◽  
◽  
Cheng-An Fu ◽  
Jia-Zhen Que
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Data Structures ◽  
Processing Time ◽  
Pattern Mining ◽  
Periodic Pattern ◽  
Performance Study ◽  
Periodic Patterns ◽  
Memory Space ◽  
Suffix Trie

Periodic pattern mining in time series database plays an important part in data mining. However, most existing algorithms consider only the count of each item, but do not consider about the value of each item. To consider the value of each item on periodic pattern mining in time series databases, Chanda et al. proposed an algorithm called WPPM. In their algorithm, they construct the suffix trie to store the candidate pattern at first. However, the suffix trie would use too much storage space. In order to decrease the processing time for constructing the data structure, in this paper, we propose two data structures to store the candidates. The first data structure is Weighted Paired Matrix. After scanning the database, we will transform the database into the matrix type, and it is used for the second data structures. Therefore, our algorithm not only can decrease the usage of the memory space, but also the processing time. Because we do not need to use so much time to construct so many nodes and edges. Moreover, wealso consider the case of incremental mining for the increase of the data length. From the performance study, we show that our proposed algorithm based on the Weighted Direction Graphis more efficient than the WPPMalgorithm.

Mining Dense Periodic Patterns in Time Series Databases

2008 ◽  
pp. 44-62
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Real Life ◽  
Search Space ◽  
Detection Algorithm ◽  
Limited Range ◽  
Periodic Pattern ◽  
Periodic Patterns ◽  
Pruning Strategy ◽  
Synthetic Datasets

In this chapter, we describe a new periodicity detection algorithm to efficiently discover short period patterns that may exist in only a limited range of the time series. We refer to these patterns as the dense periodic patterns, where the periodicity is focused on part of the time series. We present a dense periodic pattern mining algorithm called DPMiner to find dense periodic patterns, and design a pruning strategy to limit the search space to the feasible periods. Experimental results on both real-life and synthetic datasets indicate that DPMiner is both scalable and efficient.

scholarly journals HOVA-FPPM: Flexible Periodic Pattern Mining in Time Series Databases Using Hashed Occurrence Vectors and Apriori Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Muhammad Fasih Javed ◽  
Waqas Nawaz ◽  
Kifayat Ullah Khan
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Hash Table ◽  
Pattern Generation ◽  
Tree Structure ◽  
Periodic Pattern ◽  
Periodic Patterns ◽  
Tree Data ◽  
Computationally Intensive ◽  
Tree Data Structure

Finding flexible periodic patterns in a time series database is nontrivial due to irregular occurrence of unimportant events, which makes it intractable or computationally intensive for large datasets. There exist various solutions based on Apriori, projection, tree, and other techniques to mine these patterns. However, the existence of constant size tree structure, i.e., suffix tree, with extra information in memory throughout the mining process, redundant and invalid pattern generation, limited types of mined flexible periodic patterns, and repeated traversal over tree data structure for pattern discovery, results in unacceptable space and time complexity. In order to overcome these issues, we introduce an efficient approach called HOVA-FPPM based on Apriori approach with hashed occurrence vectors to find all types of flexible periodic patterns. We do not rely on complex tree structure rather manage necessary information in a hash table for efficient lookup during the mining process. We measured the performance of our proposed approach and compared the results with the baseline approach, i.e., FPPM. The results show that our approach requires lesser time and space, regardless of the data size or period value.

scholarly journals Discovering Periodic Patterns in Time Series from Twitter Data Set

2020 ◽  
Vol 9 (4) ◽  
pp. 92-102
Keyword(s):  
Time Series ◽  
Periodic Pattern ◽  
Periodic Patterns ◽  
Time Intervals ◽  
Data Set ◽  
Periodic Behavior ◽  
Expensive Process ◽  
Monotonic Property ◽  
Pruning Technique ◽  
Regular Patterns

The important class of regularities that exist in a time series is nothing but the Partial periodic patterns. These patterns have key properties such as starting, stopping, and restartinganywhere− within a series. Partial periodic patterns areclassifiedinto two types: (i) regular patterns− exhibiting periodic behavior throughout a series with some exceptions and( ii) periodic patterns exhibiting periodic behavior only for particular time intervals within a series. We have focused primarily on finding regular patterns during past studies on partial periodic search. The knowledge pertaining to periodic patterns cannot be ignored. This is because useful information pertaining to seasonal or time-based associations between events is provided bythem. Because of the foll o wi n g two main reasons, finding periodic patterns is a non-trivial task. (i) Each periodic pattern is associated with time-based information pertaining to its durations of periodic appearances in a series. Since the information can vary within and across patterns, obtaining this information ischallenging. (ii) As they do not satisfy the anti-monotonic property, finding all periodic patterns is a computationally expensive process. In this paper, periodic pattern model is proposed by addressing the above issues. Periodic Pattern growth algorithm along with an efficient pruning technique is also proposed to discover these patterns. The results through Experimentation have shown that Periodic patterns canbe really useful and it has also proven that our algorithm isnoteworthy.

Effective periodic pattern mining in time series databases

2013 ◽  
Vol 40 (8) ◽  
pp. 3015-3027 ◽  
Author(s):  
Manziba Akanda Nishi ◽  
Chowdhury Farhan Ahmed ◽  
Md. Samiullah ◽  
Byeong-Soo Jeong
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Periodic Pattern

scholarly journals Mining Fuzzy Time Interval Periodic Patterns in Smart Home Data

2018 ◽  
Vol 8 (5) ◽  
pp. 3374
Author(s):  
Imam Mukhlash ◽  
Desna Yuanda ◽  
Mohammad Iqbal
Keyword(s):  
Data Mining ◽  
Smart Home ◽  
Pattern Mining ◽  
Technology Development ◽  
Sensor Data ◽  
Mining Machine ◽  
Periodic Pattern ◽  
Time Interval ◽  
Periodic Patterns ◽  
Using Data

A convergence of technologies in data mining, machine learning, and a persuasive computer has led to an interest in the development of smart environment to help human with functions, such as monitoring and remote health interventions, activity recognition, energy saving. The need for technology development was confirmed again by the aging population and the importance of individual independent in their own homes. Pattern mining on sensor data from smart home is widely applied in research such as using data mining. In this paper, we proposed a periodic pattern mining in smart house data that is integrated between the FP-Growth PrefixSpan algorithm and a fuzzy approach, which is called as fuzzy-time interval periodic patterns mining. Our purpose is to obtain the periodic pattern of activity at various time intervals. The simulation results show that the resident activities can be recognized by analyzing the triggered sensor patterns, and the impacts of minimum support values to the number of fuzzy-time-interval periodic patterns generated. Moreover, fuzzy-time-interval periodic patterns that are generated encourages to find daily or anomalies resident’s habits.

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

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