A Design of Intelligent Pre-fetching Materialized Views Mechanism for Enhancing Summary Queries on Data Warehouses

To build up a materialized view that perfectly satisfies the need of the specific enterprise it serves is now the biggest challenge especially when it comes to larger and larger scale enterprises as well as more and more complicated and yet necessary socio-economical information. In this paper, we shall develop an Intelligent Materialized VIews Pre-fetching mechanism, also known as an IMVIP, from the characteristics of affinity grouping so as to enhance the efficiency of summary data warehouse querying. The IMVIP mechanism consists of the following two methods: the Apriori-Model association method and the Linear Structure Relation. The Apriori-Model association method explores and deduces the combination of the relations among individual user session. It is especially suitable for applications where the combinations of the relations are to be explored among multi-objective queries made by more than one decision maker. On the other hand, the Linear Structure Relation Model develops a set of principles as to the explorations into the deduced relation combination above with an aim to constructing a series of causal-effect association rules. Thus, we can not only pre-fetch and materialize views that really satisfy the needs of the decision makers so as to enhance the efficiency of summary data warehouse queries but also build up intelligent query paths according to the cause-and-effect association rules in order to attain the goal of providing helpful suggestions for decision-making

Adaptive Ttwo-phase spatial association rules mining method

Since huge amounts of spatial data can be easily collected from various applications, ranging from remote sensing technology to geographical information system, the extraction and comprehension of spatial knowledge is a more and more important task. Many excellent studies on Remote Sensed Image (RSI) have been conducted for potential relationships of crop yield. However, most of them suffer from the performance problem because their techniques for mining association rules are based on Apriori algorithm. In this paper, two efficient algorithms, two-phase spatial association rules mining and adaptive two-phase spatial association rules mining, are proposed for address the above problem. Both methods primarily conduct two phase algorithms by creating Histogram Generators for fast generating coarse-grained spatial association rules, and further mining the fine-grained spatial association rules w.r.t the coarse-grained frequently patterns obtained in the first phase. Adaptive two-phase spatial association rules mining method conducts the idea of partition on an image for efficiently quantizing out non-frequent patterns and thus facilitate the following two phase process. Such two-phase approaches save much computations and will be shown by lots of experimental results in the paper.Facultad de Informátic

An On-Line Personalized Promotion Decision Support System for Electronic Commerce

With the development of the Internet and Electronic Commerce (EC), enterprises have overcome the space and time barriers and are now capable of serving customers electronically. However, it is a great challenge to attract and retain the customers over Internet. One approach is to provide the responsive personalized service to satisfy the customer demand and promote sales at the first time. Hence, in this paper, we propose a decision support system which develops best promotion products based on combinations of different marketing strategies, pricing strategies, and customer behaviors evaluated in terms of multiple criteria. Data mining techniques are utilized to help the business discover patterns to develop on-line sales promotion products for each customer for enhancing customer satisfaction and loyalty. The proposed system consists of four components: (1) establishing marketing strategies, (2) promotion pattern model, (3) personalized promotion products, and (4) on-line transaction model. A simple example is given to illustrate the implementation and application of proposed decision support system

Adaptive Ttwo-phase spatial association rules mining method

Since huge amounts of spatial data can be easily collected from various applications, ranging from remote sensing technology to geographical information system, the extraction and comprehension of spatial knowledge is a more and more important task. Many excellent studies on Remote Sensed Image (RSI) have been conducted for potential relationships of crop yield. However, most of them suffer from the performance problem because their techniques for mining association rules are based on Apriori algorithm. In this paper, two efficient algorithms, two-phase spatial association rules mining and adaptive two-phase spatial association rules mining, are proposed for address the above problem. Both methods primarily conduct two phase algorithms by creating Histogram Generators for fast generating coarse-grained spatial association rules, and further mining the fine-grained spatial association rules w.r.t the coarse-grained frequently patterns obtained in the first phase. Adaptive two-phase spatial association rules mining method conducts the idea of partition on an image for efficiently quantizing out non-frequent patterns and thus facilitate the following two phase process. Such two-phase approaches save much computations and will be shown by lots of experimental results in the paper.Facultad de Informátic

River catchment rainfall series analysis using additive Holt–Winters method

Climate change is receiving more attention from researchers as the frequency of occurrence of severe natural disasters is getting higher. Tropical countries like Malaysia have no distinct four seasons; rainfall has become the popular parameter to assess climate change. Conventional ways that determine rainfall trends can only provide a general result in single direction for the whole study period. In this study, rainfall series were modelled using additive Holt–Winters method to examine the rainfall pattern in Langat River Basin, Malaysia. Nine homogeneous series of more than 25 years data and less than 10% missing data were selected. Goodness of fit of the forecasted models was measured. It was found that seasonal rainfall model forecasts are generally better than the monthly rainfall model forecasts. Three stations in the western region exhibited increasing trend. Rainfall in southern region showed fluctuation. Increasing trends were discovered at stations in the south-eastern region except the seasonal analysis at station 45253. Decreasing trend was found at station 2818110 in the east, while increasing trend was shown at station 44320 that represents the north-eastern region. The accuracies of both rainfall model forecasts were tested using the recorded data of years 2010–2012. Most of the forecasts are acceptable

The Relationships among Chinese Practicing Teachers ’ Epistemic Beliefs, Pedagogical Beliefs and Their Beliefs about the Use of ICT

This study aimed to investigate the relationships among practicing teachers ’ epistemic beliefs, pedagogical beliefs and their beliefs about the use of ICT through survey methodology. Participants were 396 high school practicing teachers from mainland China. The path analysis results analyzed via structural equation modelling technique indicated that the systemic relationships among these three types of beliefs were nested. Specifically, teachers ’ sophisticated beliefs about the source of knowledge were aligned with constructivist pedagogical beliefs and constructivist use of ICT, with one belief highly related to another

A Four-Stage Data Augmentation Approach to ResNet-Conformer Based Acoustic Modeling for Sound Event Localization and Detection

In this paper, we propose a novel four-stage data augmentation approach to ResNet-Conformer based acoustic modeling for sound event localization and detection (SELD). First, we explore two spatial augmentation techniques, namely audio channel swapping (ACS) and multi-channel simulation (MCS), to deal with data sparsity in SELD. ACS and MDS focus on augmenting the limited training data with expanding direction of arrival (DOA) representations such that the acoustic models trained with the augmented data are robust to localization variations of acoustic sources. Next, time-domain mixing (TDM) and time-frequency masking (TFM) are also investigated to deal with overlapping sound events and data diversity. Finally, ACS, MCS, TDM and TFM are combined in a step-by-step manner to form an effective four-stage data augmentation scheme. Tested on the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 data sets, our proposed augmentation approach greatly improves the system performance, ranking our submitted system in the first place in the SELD task of DCASE 2020 Challenge. Furthermore, we employ a ResNet-Conformer architecture to model both global and local context dependencies of an audio sequence to yield further gains over those architectures used in the DCASE 2020 SELD evaluations.Comment: 12 pages, 8 figure

Knockdown of PsbO leads to induction of HydA and production of photobiological H2 in the green alga Chlorella sp. DT

Green algae are able to convert solar energy to H2 via the photosynthetic electron transport pathway under certain conditions. Algal hydrogenase (HydA, encoded by HYDA) is in charge of catalyzing the reaction: 2H+ + 2e− ↔ H2 but usually inhibited by O2, a byproduct of photosynthesis. The aim of this study was to knockdown PsbO (encoded by psbO), a subunit concerned with O2 evolution, so that it would lead to HydA induction. The alga, Chlorella sp. DT, was then transformed with short interference RNA antisense-psbO (siRNA-psbO) fragments. The algal mutants were selected by checking for the existence of siRNA-psbO fragments in their genomes and the low amount of PsbO proteins. The HYDA transcription and the HydA expression were observed in the PsbO-knockdown mutants. Under semi-aerobic condition, PsbO-knockdown mutants could photobiologically produce H2 which increased by as much as 10-fold in comparison to the wild type