Towards an Intelligent OLAP System Facing Sparse Problems

2014 ◽  
Vol 6 (4) ◽  
pp. 41-57
Author(s):  
Rania Koubaa ◽  
Eya Ben Ahmed ◽  
Faiez Gargouri
Keyword(s):  
Association Rules ◽  
Low Frequency ◽  
Decision Makers ◽  
Specific Class ◽  
Data Warehouses ◽  
New Approach ◽  
Useful Knowledge ◽  
Rare Association ◽  
Log Files ◽  
Sparse Problems

Exploring intelligent data stored in data warehouses may efficiently assist the knowledge-seeker in his decision process. Such traced information related to performed analysis by decision-makers on data warehouses are stored in OLAP log files. These files contain useful knowledge about the analysts' preferences. Sometimes, some formulated queries provide no results. Such a dilemma is known as the sparsity problem. In this paper, to overcome this limitation in user-centric data warehouses, the authors focus on a specific class of preferences, namely the conflicting preferences. Indeed, a conflicting preference describes a low frequency preference stored in OLAP log files, so that it is considered as tailored to given analysts. Such preferences are characterized by their rarity. To deal with this issue, the authors introduce a new approach to discover these preferences through mining of rare association rules using a new introduced method for generating the N highest confidence rare association rules. The derived rare preferences will be used to reformulate the launched query avoiding an empty result. The carried out experiments on their built online recruitment data warehouse point out the efficiency of their approach.

Towards Collaborative Multidimensional Query Recommendation with Triadic Association Rules

2015 ◽  
Vol 7 (3) ◽  
pp. 17-35 ◽  
Author(s):  
Sid Ali Selmane ◽  
Omar Boussaid ◽  
Fadila Bentayeb
Keyword(s):  
Association Rules ◽  
End Users ◽  
New Approach ◽  
Query Log ◽  
Log Files ◽  
Query Logs ◽  
On Line ◽  
Synthetic Datasets ◽  
Triadic Context

This paper describes a new personalization process for decisional queries through a new approach based on triadic association rules mining. This process exploits the decision query log files of end users and follows these five steps: (1) generation of a triadic context from the multidimensional query logs of OLAP1 query analysis server; (2) mapping the triadic context into the dyadic one; (3) computation of (conventional) dyadic association rules; (4) generation of triadic association rules through a factorization process of dyadic ones and convey a richer semantics. The aim of the personalization approach which is based on triadic rules is to recommend new decision queries to OLAP end users sharing some common properties. This paper aims at helping this class of users by recommending them personalized OLAP queries that they might use in their future OLAP sessions. To validate the approach, the authors developed a software prototype called P-TRIAR (Personalization based on TRIadic Association Rules) which extracts two types of triadic association rules from decision query log files. The first type of triadic rules will serve to the recommending queries by taking the collaborative aspect of OLAP users into account. The second type of triadic rules will enrich user queries. Preliminary experiments were conducted on both real and synthetic datasets to assess the quality of the recommendations in term of precision and recall measures, as well as the performance of their on-line computation.

scholarly journals Market basket analysis with association rules in the retail sector using Orange. Case Study: Appliances Sales Company

2021 ◽  
Vol 24 (2) ◽  
Author(s):  
Marcos Martinez ◽  
Belén Escobar ◽  
Garcia-Diaz Maria-Elena ◽  
Diego P. Pinto-Roa
Keyword(s):  
Decision Making ◽  
Inventory Management ◽  
Association Rules ◽  
Retail Sector ◽  
New Approach ◽  
Useful Knowledge ◽  
Sales Company ◽  
Sporting Goods ◽  
Home Goods

This research is conducted to analyze the shopping basket by using association rules in the retail area, more specifically in a home goods sales company such as appliances, computer items, furniture, and sporting goods, among others. With the rise of globalization and the advancement of technology, retail companies are constantly struggling to maintain and raise their profits, as well ordering the products and services that the customer wants to obtain. In this sense, they need a new approach to identify different objectives in order to be more competitive and successful, looking for new decision-making strategies. To achieve this goal, and to obtain clear and efficient strategies, by providing large amounts of data collected in business transactions, the need arises to intelligently analyze such data in order to extract useful knowledge that will support decision-making and, an understanding of the association patterns that occur in sales-customer behavior. Predicting which product will make the most profit, products that are sold together, this type of information is of great value for storing products in inventory. Knowing when a product is out of fashion can support inventory management effectively. In this sense, this work presents the rules of association of products obtained by analyzing the data with the FPGrowth algorithm using the Orange tool.

scholarly journals POST-MINING OF ASSOCIATION RULES USING ONTOLOGIES AND RULE SCHEMAS

2015 ◽  
pp. 196-200
Author(s):  
R. SUBASH CHADRA BOSE ◽  
R. SIVAKUMAR
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Post Processing ◽  
Rule Mining ◽  
New Approach ◽  
Useful Knowledge ◽  
General Terms ◽  
User Knowledge

Knowledge discovery and databases (KDD) deals with the overall process of discovering useful knowledge from data. Data mining is a particular step in this process by applying specific algorithms for extracting hidden fact in the data. Association rule mining is one of the data mining techniques that generate a large number of rules. Several methods have been proposed in the literature to filter and prune the discovered rules to obtain only interesting rules in order to help the decision-maker in a business process. We propose a new approach to integrate user knowledge using ontologies and rule schemas at the stage of post-mining of association rules. General Terms- Lattice, Post-processing, pruning, itemset .

Exploring Insights through Visualization of Association Rules from Text Mining Statistics

2013 ◽  
Vol 310 ◽  
pp. 567-571
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Text Mining ◽  
Association Rules ◽  
Decision Makers ◽  
Analysis Of Knowledge ◽  
Strength Of Association ◽  
The Way

Visualization is an important technique for analysis of knowledge derived from text mining. While different approaches exist for visualization, this paper presents a novel way of visualizing the strength of association between multiple terms that summarizes association in the form of a matrix. This approach is expected to improve the way decision makers analyze insights from text mining.

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

2021 ◽  
Vol 30 (04) ◽  
pp. 2150018
Author(s):  
Anindita Borah ◽  
Bhabesh Nath
Keyword(s):  
Air Pollution ◽  
Association Rules ◽  
Air Pollutants ◽  
Pattern Mining ◽  
Environmental Parameters ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rare Association ◽  
Support Threshold ◽  
Complete Set

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

Poststack impedance inversion considering the diffractive component of the wavefield

Geophysics ◽  
2020 ◽  
Vol 85 (1) ◽  
pp. R11-R28 ◽  
Author(s):  
Kun Xiang ◽  
Evgeny Landa
Keyword(s):  
Specular Reflection ◽  
Low Frequency ◽  
Velocity Model ◽  
Forward Modeling ◽  
Small Scale ◽  
Fracture Detection ◽  
Frequency Model ◽  
New Approach ◽  
Impedance Inversion ◽  
Diffraction Imaging

Seismic diffraction waveform energy contains important information about small-scale subsurface elements, and it is complementary to specular reflection information about subsurface properties. Diffraction imaging has been used for fault, pinchout, and fracture detection. Very little research, however, has been carried out taking diffraction into account in the impedance inversion. Usually, in the standard inversion scheme, the input is the migrated data and the assumption is taken that the diffraction energy is optimally focused. This assumption is true only for a perfectly known velocity model and accurate true amplitude migration algorithm, which are rare in practice. We have developed a new approach for impedance inversion, which takes into account diffractive components of the total wavefield and uses the unmigrated input data. Forward modeling, designed for impedance inversion, includes the classical specular reflection plus asymptotic diffraction modeling schemes. The output model is composed of impedance perturbation and the low-frequency model. The impedance perturbation is estimated using the Bayesian approach and remapped to the migrated domain by the kinematic ray tracing. Our method is demonstrated using synthetic and field data in comparison with the standard inversion. Results indicate that inversion with taking into account diffraction can improve the acoustic impedance prediction in the vicinity of local reflector discontinuities.

scholarly journals Capital structure construct: a new approach to behavioral finance

2019 ◽  
Vol 16 (4) ◽  
pp. 86-97
Author(s):  
Jose Anselmo Perez Reyes ◽  
Montserrat Reyna Miranda ◽  
Jorge Vera-Martínez
Keyword(s):  
Capital Structure ◽  
Behavioral Finance ◽  
Latent Variables ◽  
Cognitive Biases ◽  
Decision Makers ◽  
Financial Behavior ◽  
Chief Financial Officers ◽  
New Approach ◽  
Financial Decision ◽  
Construct Dimensions

Within the framework of behavioral finance, this research shows that financial behavior can be assessed as a cognitive construct. Using certain variables, a multidimensional “cognitive finance” construct can thus be established. Through a technological – psychometric type design with descriptive data analysis, a factor analysis is presented to determine which latent variables tend to charge significantly in order to assess the validity of the dimensions comprising the construct of capital structure and explore its dimensions in relation to financial theory. A 44-item questionnaire is adapted and applied to a sample of chief financial officers from diverse public and nonpublic companies in Mexico. The analysis reveals the existence of four construct dimensions consistent with corporate financial theory. The model helps to explain how decision-makers react to uncertainty and environmental conditions, directly affecting the valuation of firm’s losses or earnings. As evidenced by the results, application of the Item Response Theory to the field of behavioral finance could open up new avenues to the study of cognitive biases, involved in the financial decision-making process. Thus, this implies that behavioral finance can also be treated as “cognitive finance.”

scholarly journals Low Frequency Oscillations drive EEG's complexity changes during wakefulness and sleep

2021 ◽  
Author(s):  
Joaquin Gonzalez ◽  
Diego M. Mateos ◽  
Matias Cavelli ◽  
Alejandra Mondino ◽  
Claudia Pascovich ◽  
...  
Keyword(s):  
Slow Wave Sleep ◽  
Low Frequency ◽  
Neuronal Population ◽  
Complexity Measures ◽  
Low Frequencies ◽  
New Approach ◽  
Frequency Bands ◽  
Cortical Oscillations ◽  
Brain Signals ◽  
Low Frequency Oscillations

Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

Sign in / Sign up

Export Citation Format

Share Document