Efficiently Updating the Discovered Sequential Patterns for Sequence Modification

2016 ◽  
Vol 26 (08) ◽  
pp. 1285-1313 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Wensheng Gan ◽  
Philippe Fournier-Viger ◽  
Tzung-Pei Hong
Keyword(s):  
Pattern Mining ◽  
State Of The Art ◽  
Poor Performance ◽  
Sequential Patterns ◽  
Batch Mode ◽  
Dynamic Databases ◽  
The Cost ◽  
Mining Algorithms ◽  
Sequence Modification ◽  
Over Time

Mining sequential patterns (SPs) is a popular data mining task, which consists in finding interesting, unexpected, and useful patterns in sequence databases. It has several applications in many domains. However, most sequential pattern mining algorithms assume that databases are static, i.e. that they do not change over time. But in real-word applications, sequences are often modified. Thus, it is an important issue to design algorithms for updating SPs in a dynamic database environment. Although some algorithms have been proposed to maintain SPs in dynamic databases, these algorithms may have poor performance, especially when databases contain long sequences or a large number of sequences. This paper addresses this issue by proposing a novel dynamic mining approach named PreFUSP-TREE-MOD to address the problem of maintaining and updating discovered SPs when sequences in a database are modified. The proposed approach adopts the previously proposed pre-large concept using two support thresholds, to avoid scanning the database when possible, for updating the set of discovered patterns. Due to the pruning properties of the pre-large concept, the PreFUSP-TREE-MOD maintenance algorithm can effectively reduce the cost of database scans to maintain and update the built FUSP-tree for sequence modification. When the number of modified sequences is less than the safety bound of the pre-large concept, the proposed maintenance algorithm outperforms traditional SPM algorithms in batch mode, and the state-of-the-art maintenance algorithm in terms of execution time and number of tree nodes.

scholarly journals Incrementally updating the high average-utility patterns with pre-large concept

2020 ◽  
Vol 50 (11) ◽  
pp. 3788-3807
Author(s):  
Jerry Chun-Wei Lin ◽  
Matin Pirouz ◽  
Youcef Djenouri ◽  
Chien-Fu Cheng ◽  
Usman Ahmed
Keyword(s):  
State Of The Art ◽  
The State ◽  
Batch Mode ◽  
Itemset Mining ◽  
The Past ◽  
Dynamic Databases ◽  
Speed Up ◽  
Average Utility ◽  
High Utility ◽  
High Utility Patterns

Abstract High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

Updating the Built Prelarge Fast Updated Sequential Pattern Trees with Sequence Modification

2015 ◽  
Vol 11 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Wensheng Gan ◽  
Tzung-Pei Hong ◽  
Jingliang Zhang
Keyword(s):  
Critical Issue ◽  
Decision Makers ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Large Database ◽  
The Past ◽  
Real World Applications ◽  
Dynamic Databases ◽  
Very Large Database ◽  
Sequence Modification

Mining useful information or knowledge from a very large database to aid managers or decision makers to make appropriate decisions is a critical issue in recent years. Sequential patterns can be used to discover the purchased behaviors of customers or the usage behaviors of users from Web log data. Most approaches process a static database to discover sequential patterns in a batch way. In real-world applications, transactions or sequences in databases are frequently changed. In the past, a fast updated sequential pattern (FUSP)-tree was proposed to handle dynamic databases whether for sequence insertion, deletion or modification based on FUP concepts. Original database is required to be re-scanned if it is necessary to maintain the small sequences which was not kept in the FUSP tree. In this paper, the prelarge concept was adopted to maintain and update the built prelarge FUSP tree for sequence modification. A prelarge FUSP tree is modified from FUSP tree for preserving not only the frequent 1-sequences but also the prelarge 1-sequences in the tree structure. The PRELARGE-FUSP-TREE-MOD maintenance algorithm is proposed to reduce the rescans of the original database due to the pruning properties of prelarge concept. When the number of modified sequences is smaller than the safety bound of the prelarge concept, better results can be obtained by the proposed PRELARGE-FUSP-TREE-MOD maintenance algorithm for sequence modification in dynamic databases.

Mining Closed Sequential Patterns in Progressive Databases

2013 ◽  
Vol 12 (03) ◽  
pp. 1350024
Author(s):  
R. B. V. Subramanyam ◽  
A. Suresh Rao ◽  
Ramesh Karnati ◽  
Somaraju Suvvari ◽  
D. V. L. N. Somayajulu
Keyword(s):  
Pattern Mining ◽  
Synthetic Data ◽  
Window Size ◽  
Search Space ◽  
Sequential Patterns ◽  
Data Sets ◽  
Time Stamp ◽  
Algorithmic Approach ◽  
Dynamic Databases ◽  
Search Space Pruning

Previous studies of Mining Closed Sequential Patterns suggested several heuristics and proposed some computationally effective techniques. Like, Bidirectional Extension with closure checking schemas, Back scan search space pruning, and scan skip optimization used in BIDE (BI-Directional Extension) algorithm. Many researchers were inspired with the efficiency of BIDE, have tried to apply the technique implied by BIDE to various kinds of databases; we toofelt that it can be applied over progressive databases. Without tailoring BIDE, it cannot be applied to dynamic databases. The concept of progressive databases explores the nature of incremental databases by defining the parameters like, Period of Interest (POI), user defined minimum support. An algorithm PISA (Progressive mIning Sequential pAttern mining) was proposed by Huang et al. for finding all sequential patterns over progressive databases. The structure of PISA helps in space utilization by limiting the height of the tree, to the length of POI and this issue is also a motivation for further improvement in this work. In this paper, a tree structure LCT (Label, Customer-id, and Time stamp) is proposed, and an approach formining closed sequential patterns using closure checking schemas across the progressive databases concept. The significance of LCT structure is, confining its height to a maximum of two levels. The algorithmic approach describes that the window size can be increased by one unit of time. The complexity of the proposed algorithmic approach is also analysed. The approach is validated using synthetic data sets available in Internet and shows a better performance in comparison to the existing methods.

scholarly journals Improved Strategy for High-Utility Pattern Mining Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Le Wang ◽  
Shui Wang ◽  
Haiyan Li ◽  
Chunliang Zhou
Keyword(s):  
Pattern Mining ◽  
State Of The Art ◽  
Search Space ◽  
Research Topics ◽  
Main Research ◽  
Mining Algorithm ◽  
Temporal Efficiency ◽  
High Utility ◽  
High Utility Patterns ◽  
Mining Algorithms

High-utility pattern mining is a research hotspot in the field of pattern mining, and one of its main research topics is how to improve the efficiency of the mining algorithm. Based on the study on the state-of-the-art high-utility pattern mining algorithms, this paper proposes an improved strategy that removes noncandidate items from the global header table and local header table as early as possible, thus reducing search space and improving efficiency of the algorithm. The proposed strategy is applied to the algorithm EFIM (EFficient high-utility Itemset Mining). Experimental verification was carried out on nine typical datasets (including two large datasets); results show that our strategy can effectively improve temporal efficiency for mining high-utility patterns.

The cost of homelessness: Assessing change over time

2003 ◽  
Author(s):  
M. Spano ◽  
P. Toro ◽  
M. Goldstein
Keyword(s):  
Change Over Time ◽  
The Cost ◽  
Over Time

The Internet Trap

2018 ◽  
Author(s):  
Matthew Hindman
Keyword(s):  
The Internet ◽  
Net Neutrality ◽  
The Public ◽  
Attention Economy ◽  
Digital News ◽  
The Cost ◽  
Over Time

The Internet was supposed to fragment audiences and make media monopolies impossible. Instead, behemoths like Google and Facebook now dominate the time we spend online—and grab all the profits from the attention economy. This book explains how this happened. It sheds light on the stunning rise of the digital giants and the online struggles of nearly everyone else—and reveals what small players can do to survive in a game that is rigged against them. The book shows how seemingly tiny advantages in attracting users can snowball over time. The Internet has not reduced the cost of reaching audiences—it has merely shifted who pays and how. Challenging some of the most enduring myths of digital life, the book explains why the Internet is not the postindustrial technology that has been sold to the public, how it has become mathematically impossible for grad students in a garage to beat Google, and why net neutrality alone is no guarantee of an open Internet. It also explains why the challenges for local digital news outlets and other small players are worse than they appear and demonstrates what it really takes to grow a digital audience and stay alive in today's online economy. The book shows why, even on the Internet, there is still no such thing as a free audience.

scholarly journals Costs and Benefits of Autonomous Shipping—A Literature Review

2021 ◽  
Vol 11 (10) ◽  
pp. 4553
Author(s):  
Ewelina Ziajka-Poznańska ◽  
Jakub Montewka
Keyword(s):  
Literature Review ◽  
Business Models ◽  
Financial Models ◽  
State Of The Art ◽  
Costs And Benefits ◽  
Capital Costs ◽  
Research Activities ◽  
Benefit Estimation ◽  
Autonomous Ship ◽  
The Cost

The development of autonomous ship technology is currently in focus worldwide and the literature on this topic is growing. However, an in-depth cost and benefit estimation of such endeavours is in its infancy. With this systematic literature review, we present the state-of-the-art system regarding costs and benefits of the operation of prospective autonomous merchant ships with an objective for identifying contemporary research activities concerning an estimation of operating, voyage, and capital costs in prospective, autonomous shipping and vessel platooning. Additionally, the paper outlines research gaps and the need for more detailed business models for operating autonomous ships. Results reveal that valid financial models of autonomous shipping are lacking and there is significant uncertainty affecting the cost estimates, rendering only a reliable evaluation of specific case studies. The findings of this paper may be found relevant not only by academia, but also organisations considering to undertake a challenge of implementing Maritime Autonomous Surface Ships in their operations.

scholarly journals A review on the properties, reinforcing effects, and commercialization of nanomaterials for cement-based materials

2020 ◽  
Vol 9 (1) ◽  
pp. 303-322 ◽  
Author(s):  
Zhifang Zhao ◽  
Tianqi Qi ◽  
Wei Zhou ◽  
David Hui ◽  
Cong Xiao ◽  
...  
Keyword(s):  
Cement Hydration ◽  
State Of The Art ◽  
Research Progress ◽  
Market Survey ◽  
Future Perspectives ◽  
Nano Silica ◽  
Physical Processes ◽  
Existing Problems ◽  
Cement Based Materials ◽  
The Cost

AbstractThe behavior of cement-based materials is manipulated by chemical and physical processes at the nanolevel. Therefore, the application of nanomaterials in civil engineering to develop nano-modified cement-based materials is a promising research. In recent decades, a large number of researchers have tried to improve the properties of cement-based materials by employing various nanomaterials and to characterize the mechanism of nano-strengthening. In this study, the state of the art progress of nano-modified cement-based materials is systematically reviewed and summarized. First, this study reviews the basic properties and dispersion methods of nanomaterials commonly used in cement-based materials, including carbon nanotubes, carbon nanofibers, graphene, graphene oxide, nano-silica, nano-calcium carbonate, nano-calcium silicate hydrate, etc. Then the research progress on nano-engineered cementitious composites is reviewed from the view of accelerating cement hydration, reinforcing mechanical properties, and improving durability. In addition, the market and applications of nanomaterials for cement-based materials are briefly discussed, and the cost is creatively summarized through market survey. Finally, this study also summarizes the existing problems in current research and provides future perspectives accordingly.

scholarly journals Reactive Balance Control for Legged Robots under Visco-Elastic Contacts

2020 ◽  
Vol 11 (1) ◽  
pp. 353
Author(s):  
Thomas Flayols ◽  
Andrea Del Prete ◽  
Majid Khadiv ◽  
Nicolas Mansard ◽  
Ludovic Righetti
Keyword(s):  
Inverse Dynamics ◽  
State Of The Art ◽  
Balance Control ◽  
Contact Stiffness ◽  
Poor Performance ◽  
Admittance Control ◽  
Inverse Dynamics Control ◽  
Rigid Contact ◽  
Reactive Balance ◽  
Shed Light

Contacts between robots and environment are often assumed to be rigid for control purposes. This assumption can lead to poor performance when contacts are soft and/or underdamped. However, the problem of balancing on soft contacts has not received much attention in the literature. This paper presents two novel approaches to control a legged robot balancing on visco-elastic contacts, and compares them to other two state-of-the-art methods. Our simulation results show that performance heavily depends on the contact stiffness and the noises/uncertainties introduced in the simulation. Briefly, the two novel controllers performed best for soft/medium contacts, whereas “inverse-dynamics control under rigid-contact assumptions” was the best one for stiff contacts. Admittance control was instead the most robust, but suffered in terms of performance. These results shed light on this challenging problem, while pointing out interesting directions for future investigation.

scholarly journals Stochastic methods defeat regular RSA exponentiation algorithms with combined blinding methods

2021 ◽  
Vol 15 (1) ◽  
pp. 408-433
Author(s):  
Margaux Dugardin ◽  
Werner Schindler ◽  
Sylvain Guilley
Keyword(s):  
State Of The Art ◽  
Stochastic Methods ◽  
Rsa Algorithm ◽  
Montgomery Ladder ◽  
Channel Information ◽  
Probability Mass ◽  
The Cost ◽  
Noisy Measurements ◽  
Mass Functions ◽  
Larger Sample

Abstract Extra-reductions occurring in Montgomery multiplications disclose side-channel information which can be exploited even in stringent contexts. In this article, we derive stochastic attacks to defeat Rivest-Shamir-Adleman (RSA) with Montgomery ladder regular exponentiation coupled with base blinding. Namely, we leverage on precharacterized multivariate probability mass functions of extra-reductions between pairs of (multiplication, square) in one iteration of the RSA algorithm and that of the next one(s) to build a maximum likelihood distinguisher. The efficiency of our attack (in terms of required traces) is more than double compared to the state-of-the-art. In addition to this result, we also apply our method to the case of regular exponentiation, base blinding, and modulus blinding. Quite surprisingly, modulus blinding does not make our attack impossible, and so even for large sizes of the modulus randomizing element. At the cost of larger sample sizes our attacks tolerate noisy measurements. Fortunately, effective countermeasures exist.

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