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

Epithelial PD-L1 expression at tumor front predicts overall survival in a cohort of oral squamous cell carcinomas from Sudan

Background We recently described the tumor immune microenvironment (TIME) in oral squamous cell carcinomas (OSCC) from Sudan by assessing the core of the lesions. However, the invasive tumor front (ITF) is the most active part of OSCC lesions; thus, TIME should also be characterized at the ITF in this patient cohort. Objectives We aimed to evaluate patterns of immune cell infiltration at the ITF in a cohort of OSCC patients from Sudan previously investigated at the tumor center and their association with clinicopathological parameters. Methods This study was performed on a prospective cohort of 22 OSCC patients attending Khartoum Dental Teaching Hospital with a median follow-up of 48 months. Inflammatory infiltrate densities of CD4-, CD8-, FoxP3-, CD20-, CD66b-, M1 (CD80/CD68)-, M2 (CD163/CD68)-, and PD-L1-positive cells were assessed at the ITF by immunohistochemistry, followed by digital quantitative analysis at the stromal and epithelial compartments separately. Histopathological parameters such as the worst pattern of invasion, differentiation, and tumor budding (TB) were also assessed. Correlations between clinicopathological parameters and survival analysis were investigated using SPSS. Results All inflammatory cell subsets investigated were found to be higher in the stromal compartment as compared to the epithelial one, except for the PD-L1+ subset. Stromal infiltration with the CD8+ cell subset was associated with low TB. Kaplan–Meier analyses identified higher epithelial and stromal CD4+ cell subsets. The presence of PD-L1 was found to be associated with unfavorable overall survival. Further, Cox's regression analysis using an age- and tumor-stage-adjusted model identified epithelial PD-L1 expression at the ITF as the only independent prognosticator. Conclusions Epithelial PD-L1 expression at the ITF was found to be an independent prognostic biomarker for OSCC in a cohort of Sudanese patients.publishedVersio

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

Translating seasonal climate forecasts into water balance forecasts for decision making

Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Resampling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way

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