Building data warehouses in the era of big data: an approach for scalable and flexible big data warehouses

During the last few years, the concept of Big Data Warehousing gained significant attention from the scientific community, highlighting the need to make design changes to the traditional Data Warehouse (DW) due to its limitations, in order to achieve new characteristics relevant in Big Data contexts (e.g., scalability on commodity hardware, real-time performance, and flexible storage). The state-of-the-art in Big Data Warehousing reflects the young age of the concept, as well as ambiguity and the lack of common approaches to build Big Data Warehouses (BDWs). Consequently, an approach to design and implement these complex systems is of major relevance to business analytics researchers and practitioners. In this tutorial, the design and implementation of BDWs is targeted, in order to present a general approach that researchers and practitioners can follow in their Big Data Warehousing projects, exploring several demonstration cases focusing on system design and data modelling examples in areas like smart cities, retail, finance, manufacturing, among others

AD-Graph: weakly supervised anomaly detection graph neural network

The main challenge faced by video-based real-world anomaly detection systems is the accurate learning of unusual events that are irregular, complicated, diverse, and heterogeneous in nature. Several techniques utilizing deep learning have been created to detect anomalies, yet their effectiveness on real-world data is often limited due to the insufficient incorporation of motion patterns. To address these problems and enhance the traditional functionality of anomaly detection systems for surveillance video data, we propose a weakly supervised graph neural-network-assisted video anomaly detection framework called AD-Graph. To identify temporal information from a series of frames, we extract 3D visual and motion features and represent these in a language-based knowledge graph format. Next, a robust clustering strategy is applied to group together meaningful neighbourhoods of the graph with similar vertices. Furthermore, spectral filters are applied to these graphs, and spectral graph theory is used to generate graph signals and detect anomalous events. Extensive experimental results over two challenging datasets, UCF-Crime and ShanghaiTech, show improvements of 0.35% and 0.78% against a state-of-the-art model

A coupled agent-based model to analyse human-drought feedbacks for agropastoralists in dryland regions

Drought is a persistent hazard that impacts the environment, people's livelihoods, access to education and food security. Adaptation choices made by people can influence the propagation of this drought hazard. However, few drought models incorporate adaptive behavior and feedbacks between adaptations and drought. In this research, we present a dynamic drought adaptation modeling framework, ADOPT-AP, which combines socio-hydrological and agent-based modeling approaches. This approach is applied to agropastoral communities in dryland regions in Kenya. We couple the spatially explicit hydrological Dryland Water Partitioning (DRYP) model with a behavioral model capable of simulating different bounded rational behavioral theories (ADOPT). The results demonstrate that agropastoralists respond differently to drought due to differences in (perceptions of) their hydrological environment. Downstream communities are impacted more heavily and implement more short-term adaptation measures than upstream communities in the same catchment. Additional drivers of drought adaptation concern socio-economic factors such as wealth and distance to wells. We show that the uptake of drought adaptation influences soil moisture (positively through irrigation) and groundwater (negatively through abstraction) and, thus, the drought propagation through the hydrological cycle

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.</p

Dynamic Facial Landmarking Selection for Emotion Recognition using Gaussian Processes

Facial features are the basis for the emotion recognition process and are widely used in affective computing systems. This emotional process is produced by a dynamic change in the physiological signals and the visual answers related to the facial expressions. An important factor in this process, relies on the shape information of a facial expression, represented as dynamically changing facial landmarks. In this paper we present a framework for dynamic facial landmarking selection based on facial expression analysis using Gaussian Processes. We perform facial features tracking, based on Active Appearance Models for facial landmarking detection, and then use Gaussian process ranking over the dynamic emotional sequences with the aim to establish which landmarks are more relevant for emotional multivariate time-series recognition. The experimental results show that Gaussian Processes can effectively fit to an emotional time-series and the ranking process with log-likelihoods finds the best landmarks (mouth and eyebrows regions) that represent a given facial expression sequence. Finally, we use the best ranked landmarks in emotion recognition tasks obtaining accurate performances for acted and spontaneous scenarios of emotional datasets

Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia–Sudan)

Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&amp;R, approximately 77 504 km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&amp;R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14 % and from 35 to 14 % for Dinder and Rahad, respectively. Cropland increased from 14 to 47 % and from 18 to 68 % in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland

Analysis of streamflow response to land use and land cover changes using satellite data and hydrological modelling: Case study of Dinder and Rahad tributaries of the Blue Nile (Ethiopia-Sudan)

Understanding the land use and land cover changes (LULCCs) and their implication on surface hydrology of the Dinder and Rahad basins (D&R, approximately 77 504km2) is vital for the management and utilization of water resources in the basins. Although there are many studies on LULCC in the Blue Nile Basin, specific studies on LULCC in the D&R are still missing. Hence, its impact on streamflow is unknown. The objective of this paper is to understand the LULCC in the Dinder and Rahad and its implications on streamflow response using satellite data and hydrological modelling. The hydrological model has been derived by different sets of land use and land cover maps from 1972, 1986, 1998 and 2011. Catchment topography, land cover and soil maps are derived from satellite images and serve to estimate model parameters. Results of LULCC detection between 1972 and 2011 indicate a significant decrease in woodland and an increase in cropland. Woodland decreased from 42 to 14% and from 35 to 14% for Dinder and Rahad, respectively. Cropland increased from 14 to 47% and from 18 to 68% in Dinder and Rahad, respectively. The model results indicate that streamflow is affected by LULCC in both the Dinder and the Rahad rivers. The effect of LULCC on streamflow is significant during 1986 and 2011. This could be attributed to the severe drought during the mid-1980s and the recent large expansion in cropland

Modelling the inundation and morphology of the seasonally flooded Mayas Wetlands in the Dinder National Park-Sudan

Understanding the spatiotemporal dynamics of surface water in varied, remote and inaccessible isolated floodplain lakes is difficult. Seasonal inundation patterns of these isolated lakes can be misestimated in a hydrodynamic model due to the short time of connectivity. The seasonal and annual variability of the Dinder River flow has great impact on what is so called Mayas wetlands, and hence, on the habitats and the ecological status of the Dinder National Park. This variability produces large morphological changes due to sediment transported within the river or from the upper catchment, which affects inflows to Mayas wetlands and floodplain inundation in general. In this paper, we investigated the morphological dimension using a quasi-3D modelling approach to support the management of the valuable Mayas wetlands ecosystems, and in particular, assessment of hydrological and morphological regime of the Dinder River as well as the Musa Maya. Six scenarios were developed and tested. The first three scenarios consider three different hydrologic conditions of average, wet and dry years under the existing system with the constructed connection canal. While the other three scenarios consider the same hydrologic conditions but under the natural system without an artificial connection canal. The modelling helps to understand the effect of human intervention (connection canal) on the Musa Maya. The comparison between the simulated scenarios concludes that the hydrodynamics and sedimentology of the Maya are driven by the two main factors: a) the hydrological variability of Dinder River; and b) deposited sediment plugs in the connection canal

Modelling the Inundation and Morphology of the Seasonally Flooded Mayas Wetlands in the Dinder National Park-Sudan

Understanding the spatiotemporal dynamics of surface water in varied, remote and inaccessible isolated floodplain lakes is difficult. Seasonal inundation patterns of these isolated lakes can be misestimated in a hydrodynamic model due to the short time of connectivity. The seasonal and annual variability of the Dinder River flow has great impact on what is so called Mayas wetlands, and hence, on the habitats and the ecological status of the Dinder National Park. This variability produces large morphological changes due to sediment transported within the river or from the upper catchment, which affects inflows to Mayas wetlands and floodplain inundation in general. In this paper, we investigated the morphological dimension using a quasi-3D modelling approach to support the management of the valuable Mayas wetlands ecosystems, and in particular, assessment of hydrological and morphological regime of the Dinder River as well as the Musa Maya. Six scenarios were developed and tested. The first three scenarios consider three different hydrologic conditions of average, wet and dry years under the existing system with the constructed connection canal. While the other three scenarios consider the same hydrologic conditions but under the natural system without an artificial connection canal. The modelling helps to understand the effect of human intervention (connection canal) on the Musa Maya. The comparison between the simulated scenarios concludes that the hydrodynamics and sedimentology of the Maya are driven by the two main factors: a) the hydrological variability of Dinder River; and b) deposited sediment plugs in the connection canal