Data Modeling

This paper discusses conceptual and logical data models for social media datasets and applications. On the one hand, we focus on the data representation requirements from the available APIs of some social network systems. On the other hand, we consider those application requirements for information manipulation. We propose a conceptual meta-model and one possible instantiation. We also give preliminary practical results considering relational and graph database systems.

Modeling the dependencies among multiple temporal attributes derived from integrated healthcare databases represents an unprecedented opportunity to support medical and administrative decisions. However, existing predictive models are not yet able to successfully anticipate health conditions based on multiple (sparse) time sequences derived from repositories of health-records. To tackle this problem, we propose new predictive models able to learn from an expressive temporal structure, a time-enriched itemset sequence, which captures both temporal and cross-attribute dependencies. Revised pattern-based models and hidden Markov models are proposed to address the properties of the target integrative temporal structures. The conducted experiments hold evidence for the utility and accuracy of the proposed predictive models to anticipate health conditions, such as the need for surgeries.

The complex physical processes controlling ceiling and visibility (for example, the formation, evolution and motion of low cloud, precipitation and fog) and the diverse seasonal and geographic influences that modulate these controls across the continental U.S. and Alaska yield an extremely difficult analysis and forecast problem. This same phenomenology significantly impacts the safety and continuity of aviation operations, making VFR (visual flight rules) flight into impacted conditions associated with IFR (instrument flight rules) the leading cause among weather-related aviation accidents in the U.S.

Healthcare insurance processes millions of claims daily, which makes it a prime target for fraud and errors. Due to the mistakes, there has been a massive increase in health insurance costs in recent years, and it's because of the payment errors made by the insurance companies while processing the claims. We describe a technology that helps detect fraud and prevent errors using machine learning. Machine Learning techniques such as Supervised and unsupervised learning, natural language processing, and deep learning analyze vast datasets to identify patterns, anomalies, and inconsistencies in claims data. Payment Errors made by insurance companies while processing claims often result in reprocessing of the claims. The extra work to reprocess the claims is known as rework. Machine Learning improves accuracy, reduces costs, streamlines claims processing, and improves customer satisfaction. In the future, Machine Learning will have the potential for realtime decision-making and greater collaboration across the industry.

UML Activity Diagrams are dynamic blueprints of how tasks, actors, and other objects (e.g., data) weave together to achieve goals. Whether you are developing an app, streamlining business operations, or simply making sense of chaos, mastering Activity Diagrams is your first step toward building effective and responsible systems. UML Activity Diagrams turn abstract plans and ideas into crystal-clear visual maps, exposing hidden bottlenecks, decision points, and opportunities for improvement.

UML class diagrams serve as one of the most powerful tools for representing systems. Class diagrams provide a precise, yet intuitive view of how a system is organized in terms of classes, attributes, relationships and operations. Through practical examples and applied modeling techniques, the textbook shows how class diagrams become critical blueprints that guide system design and implementation and support clear communication among stakeholders.

This paper explores the use of deep learning for leak localization in Water Distribution Networks (WDNs) using pressure measurements. By using a training data set including enough samples of all possible leak localizations, a Convolutional Neural Network(CNN) can be used to learn the different pressure maps that carachterized each leak localization. The generalization accuracy has validated and evaluated by means of a testing data set. All of considered training, validation, and also testing data include leak size uncertainty, nodal water demand uncertainty and sensor noise. An innovative approach is proposed to convert every pressure residuals map to an image in order to apply a CNN. In addition with the purpose of filtering the effects of uncertainty and noise a time horizon Bayesian reasoning approach is used over each time instant classification output by the CNN. The Hanoi District Metered Area (DMA) is considered as a case study to illustrate the performance of the proposed leak localization method.

The definition of the exact meaning of conceptual modeling constructs is considered a relevant issue since it contributes to their effective and appropriate use by conceptual modelers. This paper studies three related constructs that enhance the expressiveness of the UML language about associations and which still lack a complete and comprehensive study, namely, association subsetting, specialization and redefinition. It formalizes their semantics, analyses them from an ontological perspective and compares them. The semantic formalization is based on mapping the studied constructs to a basic UML layer which have a previous formal definition in the literature. Furthermore, the ontological analysis developed here is based on a formal theory of relations which is part of the Unified Foundational Ontology (UFO).

Non-technical losses (NTLs), particularly due to electricity theft, pose challenges to distribution systems in the form of revenue loss, grid instability, and safety hazards. While machine learning (ML) models have been proposed for detecting potential fraud in consumption data, most studies rely on high-resolution data from smart meters-a technology that remains unavailable in many developing countries. This study proposes a Random Forest-based ML model that identifies irregular patterns using only monthly electricity consumption data. To address the class imbalance inherent in real-world datasets, sampling techniques such as Random Under-Sampling (RUS) and Synthetic Minority Over-Sampling Technique (SMOTE) were applied. Results show that SMOTE significantly enhances model performance in terms of precision, recall, and F1-score, underscoring the importance of data balancing in NTL detection. These findings highlight the feasibility of ML-based NTL detection using low-resolution data, making it a viable solution for distribution utilities with limited infrastructure. Future research should explore alternative ML models, hybrid approaches, and diverse datasets to further improve detection accuracy.

An offshoot of research on developing methods typical business professionals can use to analyze systems for themselves, Sysperanto is being developed as an ontology codifying concepts and knowledge useful in describing and analyzing systems in organizations. Sysperanto's architecture is organized around the nine elements of the work system framework and the observation that information systems, projects, supply chains, ecommerce, and other important types of systems can be modeled as special cases of work systems. These supertype-subtype relationships provide an opportunity to organize relevant concepts economically based on the "partial inheritance conjecture," whereby most, but not all, elements, properties, and propositions for a specific work system type are inherited by more specialized work system types. This paper explains why Sysperanto or other ontologies with similar purposes require the use of partial inheritance rather than strict hierarchy.

other projects in the database. Software development data is highly variable, which often result,s in underlying trends being hidden. In order to address this problem, a method of data analysis, adapted from the financial community, is presented that permits the shape of the curve of some activity to be reduced to a few line segments, called the characteristic curve. This process is used on sample data from the NASA/GSFC Software Engineering Laboratory and has shown to be a reasonable method to understand ttie underlying process being plotted. Lines of code produced, staff hours, error reports filed, change reports filed, modules created, and modules changed are all reported weekly by project personnel or are extracted automatically from the source code libraries. Once in the data base, we can plot these as raw data weekly scatter plots or as various growth functions to show how the collected statistics change over time.

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena -which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

This paper focuses on the need for knowledge organization (KO) tools, such as library classifications, thesauri and subject heading systems, to be fully disclosed and available in the open network environment. The authors look at the place and value of traditional library knowledge organization tools in relation to the technical environment and expectations of the Semantic Web. Future requirements in this context are explored, stressing the need for KO systems to support semantic interoperability. In order to be fully shareable KO tools need to be reframed and reshaped in tenus of conceptual and data models. The authors suggest that some useful approaches to this already exist in methodological and technical developments within the fields of ontology modelling and lexicographic and terminological data interchange.

In this article we compare two contrasting methods, active set method ASM and genetic algorithms, for learning the weights in aggregation operators, such as weighted mean Ž . Ž . WM , ordered weighted average OWA , and weighted ordered weighted average Ž . WOWA . We give the formal definitions for each of the aggregation operators, explain the two learning methods, give results of processing for each of the methods and operators with simple test datasets, and contrast the approaches and results.

XPDI: A System for Expert Systems Development

This technical document describes XPDI (eXpert Product Data Interchange Station), a software environment developed by Patrice POYET and his team at CSTB in 1989-1992, aimed at integrating Artificial Intelligence (AI) and Product Model technologies within the Computer Integrated Manufacturing and Engineering (CIME) context. It functions as a meta-environment for developing system generators and shells, facilitating the creation and management of complex engineering data and processes. The text details XPDI's architecture, including its kernel and various "shells" (like COMSET, AMSET, VAMSET, DOCSET, STEPSET), which support diverse functionalities such as conceptual modeling, semantic drafting, and documentation. Furthermore, it outlines XPDI's object-oriented structure, its messaging system, and the Application Programming Interfaces (APIs) for its various components, demonstrating how it enables efficient data exchange and collaborative design. The document highlights XPDI's role in addressing challenges related to data heterogeneity and inter-operability in the IT market, particularly within the construction and engineering fields.

Production Planning and Production Control systems are some of the keys that determine the development of modern production systems. The article presents the importance of a flexibility factor in the process of manufacturing, planning, and control systems development during the 20th century. Finally, actual problems and trends in the production system design and control over the design were perceived in addition to possible feasible directions of the development of manufacturing systems, probably in the 21st century were specified. Production planning and production control are usually painstaking to be one of the most noteworthy issues in the planning and operation of a manufacturing structure. A better planning system has a significant bang on cost reduction, increased productivity, customer satisfaction, and overall spirited improvement for a product. Also, the current customer demand for prominent diversity products has put into an increase in product complications that further ...

This project focuses on predicting employee attrition using machine learning techniques. By analyzing employee data such as age, salary, job satisfaction, and work experience, the model helps identify employees who are likely to leave the company. This prediction allows organizations to take proactive steps to retain valuable employees, reduce turnover costs, and improve overall performance.

Considerando-se um contexto de busca de recursos humanos qualificados e dispostos a inovar e aprender foi criado o Módulo de Estágios do sistema Integrado de Gestão e Pesquisa da Embrapa Gado de Corte ? Pandora ? no qual este Manual está baseado. Buscando facilitar a compreensão dos usuários em relação à operacionalização desse sistema, o Manual divide-se em duas partes, uma referente aos procedimentos dos estagiários e outra, dos supervisores. Dessa forma, são utilizadas telas do sistema com os respectivos procedimentos para facilitar a compreensão dos usuários na utilização das ferramentas disponíveis.bitstream/item/77526/1/DOC191.pd

A grey wolf optimization algorithm is a newly developed metaheuristic algorithm. GWO has given a better solution to the optimization problem as compare to other swarm intelligence. It is a very simple and easy to implement this algorithm. It is considered as balanced in exploitation and exploration. GWO has a few parameters that why researches use this algorithm to solve the optimization problem. In this research a systematic literature study is carried out for studying about grey wolf optimization algorithm and its several models like hybrid, modified etc. Focus of this research is to deep investigate about Grey wolf optimization algorithm and find out issues in it.

The FAIR Data Principles are widely applied to research data. These principles have been broadly adopted by scientific and scholarly institutions to guide research data infrastructures and services, ensuring data is findable, accessible, interoperable, and reusable. However, due to their interpretation scope, it is still challenging to assess the extent to which a data infrastructure addresses the FAIR principles. The Research Data Alliance (RDA) set up the FAIR Data Maturity Model Working Group to specify the required indicators for institutions to assess their levels of FAIR compliance, producing the FAIR Data Maturity Model (RDA-FDMM) . The RDA-FDMM defines 41 FAIR indicators, organized into three classes (Essential, Important, and Useful), and five levels . We applied the RDA-FDMM to the PID service [3] of KonsortSWD ii , which aims to assign PIDs to data elements below study level (such as survey variables). Furthermore, we discuss automatic assessment using the F-UJI Tool iii , which employs RDA-FDMM and FAIRsFAIR Metrics [4] in a machine-readable fashion. At the PID service, which is based on the data registration agency da|ra [5], we manually assessed some elements at the PID service level and others at the da|ra level, using the pass-or-fail method ('yes' or 'no') questions. For the automatic assessment we adopted the F-UJI tool, a web service to automatically assess FAIRness of research data objects based on FAIR object assessment metrics . We applied the F-UJI tool to GESIS Search iv in the context of KonsortSWD, motivated by the European landscape study , which also relies on F-UJI tool and led us to improve our metadata [8]. The manual assessment results show that the PID service meets all the indicators classified as essential and most of the indicators from the classes important and useful (see Table ).

The combination of social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions in terms of their impact on the network is a powerful approach for supporting crisis de-escalation activities. In this paper, we describe how researchers used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of going from datato-model and the advantages of data-driven simulation. We conclude with a discussion of the limitations of this approach for the chosen policy domain and our anticipated future steps.

The delicate ecosystems of the Alps' subalpine forests are crucial to water supplies as well as the local and mesoscale climate regulators. Although earlier research has assessed various aspects of the water balance, there is currently a dearth of studies that directly measure every component of the water budget. Furthermore, little is understood about the frequency and impact of fog as well as how forest layout affects water balance. Using the eddy covariance technique, sap flow sensors, phenocam images, throughfall and stemflow gauges, soil moisture sensors, water discharge measurements, and a fog interception gauge, we carried out a thorough investigation of a subalpine coniferous forest at the Renon site in the Italian Alps. Furthermore, we measured the leaf area and lichen occurrence as possible canopy water storage components. Large amount of precipitation was reflected by the canopy interception in spruce and coniferous forest. Although fog alone had no effect on total water intake, it did result in a tiny but noticeable increase in throughfall during mixed fog and rain precipitation events, however this effect seemed to be less significant than in cloud forests that are tropical or subtropical. At the catchment level, the annual balance (November-October) was almost perfectly closed when all input and output components were taken into account. This paper contributes to the ecological monitoring of the Alpine forests in South Tyrol, Northern Italy.

Typically, in simulating the dynamic processes in buildings, data modeling efforts require the modeling of the building geometry, its components and the relationship between these components, as well as the modeling of the process that is under study. For example, in simulating the life cycle of a building, one must simulate the flow of materials as well as the flow of information as part of the process modeling, while a component model is needed to represent the building as an artefact. A third aspect of this modeling effort constitutes the simulation of human intervention, i.e. the decision process that might affect the nature of the building itself as well as the process that acts upon it. For example, the decision to remodel a certain component clearly affects both the component itself as well as the process of aging, when the life cycle of buildings is simulated. This paper looks at the data modeling requirements of the simulation of building life cycle within the context of the three parameters mentioned above: data model for buildings, process models, and decision models. Temporal issues in data modeling, such as versioning for components, keeping track of data that are related to change and remodeling, and buildings as temporal -spatial entities for life cycle analysis purposes are also addressed.

In recent years, leakage detection models using artificial intelligence have been widely used by researchers. In this study, acoustic sound data were recorded at 10 observation points, focusing on ductile iron pipe and vinyl polyethylene. Leakage detection models were built using recurrence plots (RPs) and a convolutional neural network (CNN). Using the same number of RPs for training data and testing data, we analyzed the effect of data variations on model performance. The results showed that our proposed approach can improve accuracy at several points, although the amount of training data information was advantageous in previous work. There were cases in previous work that had poor accuracy, but when implementing our proposed approach, it improved the accuracy to over 80% when using the 8-point model. The increase in accuracy depends on which interval is used in the test data because each interval contains information about different properties. For multi-point models, the effect of increasing the number of RPs and data variations was not clarified in the previous work. However, our study confirmed that the increase in data variation contributed more than the RP number.

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log-log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.

Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available Spatio-Temporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.

The wave of interest in data-centric applications has spawned a high variety of data models, making it extremely difficult to evaluate, integrate or access them in a uniform way. Moreover, many recent models are too specific to allow immediate comparison with the others and do not easily support incremental model design. In this paper, we introduce GSMM, a metamodel based on the use of a generic graph that can be instantiated to a concrete data model by simply providing values for a restricted set of parameters and some high-level constraints, themselves represented as graphs. In GSMM, the concept of data schema is replaced by that of constraint, which allows the designer to impose structural restrictions on data in a very flexible way. GSMM includes GSL, a graph-based language for expressing queries and constraints that besides being applicable to data represented in GSMM, in principle, can be specialised and used for existing models where no language was defined. We show some sample applications of GSMM for deriving and comparing classical data models like the relational model, plain XML data, XML Schema, and time-varying semistructured data. We also show how GSMM can represent more recent modelling proposals: the triple stores, the BigTable model and Neo4j, a graph-based model for NoSQL data. A prototype showing the potential of the approach is also described. Meta-modelling • Heterogeneous data • Graph-based data model • Graph-based constraints B Barbara Oliboni

This paper introduces a strategy for both the retrieval and analysis of linked open data (LOD) based on the use of visual tools. Retrieving and understanding data from triplestores (such as SPARQL) requires technical knowledge and proves to be challenging with large datasets, which result in an increase in the mental overload when unknown ontologies are involved in the creation of complex queries. These two problems benefit greatly from visual techniques that allow for executing them in an easier and more intuitive manner. These techniques have already been applied to each problem separately; however, we propose combining them to lower the complexity of triple-store data retrieval and empower its exploration and analysis through specific data visualizations. To demonstrate the suitability of this strategy, a web-based tool was implemented. It allows for the creation of interactive queries using node-link ontology representations, and a subsequent filtering and analysis through a configurable dashboard with different data visualizations. This paper also presents a user-centred design process and evaluation of the proposed tool.

Traditional data models often emphasize structure over context-dependent reality, leading to a loss of control by data creators. Consequently, protecting and providing on-demand access control to sensitive data remains a critical challenge in modern data management. This paper proposes Data Atomization, a framework to address these challenges by decoupling data from predefined structures, as seen in traditional data models, such as relational databases. Instead of rigid associations, our approach uses flexible ontologies to define data context, making it more adaptable. These ontologies can exist at different levels as organizational, regional, or authority-based, allowing data elements to be interconnected. This ensures data is only linked to its intended use cases, enabling independent growth of both data and its structure. By leveraging ontological querying, data atomization, and hybrid encryption, our framework provides a secure and dynamic way to manage sensitive data with context-specific access. Unlike existing models for data privacy preservation, our approach differs in representation, process, and data residency.

The research presented here is carried out within the INTERREG EU project framework, which aims to the valorisation and dissemination of the role of the Church of St. Maria di Scaria (Como, Italy). It mainly focuses on the Carloni's intervention (XVIII century), a local family of craftsmen, famous across many European cities and regions contributing to the construction of monuments and their rich decoration apparatus. The laser scanning and photogrammetric surveys have been integrated with the on-site stratigraphic analysis and with the scarcely available historical documents, in an attempt to focus on the reconstruction of the main transformations and chronological phases: BIM approach has been experimented as a way of transmitting a piece of the history of the church life to the local people and for tourist purposes. A Historic Building Information Modeling (HBIM) has been developed while investigating the potential of an object library specially generated to illustrate the st...

Purpose
Deep generative models and synthetic data generation have become essential for advancing computer-assisted diagnosis and treatment. We explore one such emerging and particularly promising application of deep generative models, namely, the generation of virtual contrast enhancement. This allows to predict and simulate contrast enhancement in breast magnetic resonance imaging (MRI) without physical contrast agent injection, thereby unlocking lesion localization and categorization even in patient populations where the lengthy, costly, and invasive process of physical contrast agent injection is contraindicated.

Approach
We define a framework for desirable properties of synthetic data, which leads us to propose the scaled aggregate measure (SAMe) consisting of a balanced set of scaled complementary metrics for generative model training and convergence evaluation. We further adopt a conditional generative adversarial network to translate from non-contrast-enhanced T1-weighted fat-saturated breast MRI slices to their dynamic contrast-enhanced (DCE) counterparts, thus learning to detect, localize, and adequately highlight breast cancer lesions. Next, we extend our model approach to jointly generate multiple DCE-MRI time points, enabling the simulation of contrast enhancement across temporal DCE-MRI acquisitions. In addition, three-dimensional U-Net tumor segmentation models are implemented and trained on combinations of synthetic and real DCE-MRI data to investigate the effect of data augmentation with synthetic DCE-MRI volumes.

Results
Conducting four main sets of experiments, (i) the variation across single metrics demonstrated the value of SAMe, and (ii) the quality and potential of virtual contrast injection for tumor detection and localization were shown. Segmentation models (iii) augmented with synthetic DCE-MRI data were more robust in the presence of domain shifts between pre-contrast and DCE-MRI domains. The joint synthesis approach of multi-sequence DCE-MRI (iv) resulted in temporally coherent synthetic DCE-MRI sequences and indicated the generative model’s capability of learning complex contrast enhancement patterns.

Conclusions
Virtual contrast injection can result in accurate synthetic DCE-MRI images, potentially enhancing breast cancer diagnosis and treatment protocols. We demonstrate that detecting, localizing, and segmenting tumors using synthetic DCE-MRI is feasible and promising, particularly considering patients where contrast agent injection is risky or contraindicated. Jointly generating multiple subsequent DCE-MRI sequences can increase image quality and unlock clinical applications assessing tumor characteristics related to its response to contrast media injection as a pillar for personalized treatment planning.

Forests affect climate parameters through impacts from biochemical and biophysical processes. However, the effects of the coniferous stands on hydrological setting in temperate regions have received little attention. This study investigated impacts of forest with different ages on water balance. The dominated species include spruce (Picea abies L.), Swiss stone pine (Pinus cembra L.) and Scots pine (Pinus sylvestris L.). During fieldwork campaign, data were collected on temperature, radiance, precipitation, evapotranspiration, throughfall, humidity and vapour pressure deficit. Data modelling reveals the relationship between the characteristics of forests (tree age, height and canopy structure) and meteorological parameters on water balance. This study supports silviculture, forest management and sustainability of Alpine environment in North Italy.

Climate plays a pivotal role in construction of relationships between coniferous forest health and water balance for efficient biosynthesis under changing meteorological variables. Here we identify that the age of the forest (young <30 years old, and old >200 years old) confers an improved ecophysiology for the maintenance of water balance through the response of trees to weather conditions (precipitation, temperature and water balance in different seasons). Global climate change, of the rise in temperatures, has an impact on the environment of mountain ecosystems in the Alps. Subalpine forests enhance partitioning precipitation inputs, with interception processes in the tree canopy determining the stability of the proportion of water reaching the ground and its quantity in the form of evaporation. These processes support global improvements in forest ecosystems at the catchment scale, as interception is influenced by the structure and density of the tree canopy and the presence of epiphytes. This study revealed that coniferous forests (spruce, fir and pine) have a significant influence on how much water is retained and discharged in the soil and plants. Interception in dense subalpine forests can account for a significant proportion of precipitation. Data analysis using Python-based modeling revealed that forest age increases biosynthesis by enhancing water fluxes. This study highlights the significance of uncovering hidden climate-environment determinants leading to improved forest hydrology and ecophysiology to enhance the biosynthesis and water balance in coniferous forests of the Alps.

Due to the difficulty of employing real data centres' infrastructure for assessing the effectiveness of energy-aware algorithms, many researchers resort to use simulation tools. These tools require precise and detailed models for virtualized data centres in order to deliver accurate results. In recent years, many models have been proposed, but most of them either do not consider energy consumption related to virtual machine (VM) migration or do not investigate some of the energy impacting components (e.g. CPU, network, storage). We propose a new model for data centre energy consumption that takes into account the previously omitted components and provides more accurate energy consumption predictions compared to other state-of-the-art solutions. We evaluate our model's accuracy in a comprehensive set of scenarios implemented in the combined GroudSim/DISSECT-CF simulator. Our analysis revealed a significant improvement in accuracy (up to 42.5%) in the modelled energy consumption compared to a similar state-ofthe-art simulator.

This paper proposes a novel multi-task statistical learning framework which aims to concurrently address all the environmental challenges in the Alps. The goal is to analyse the effects of lichen and fog on water balance. The objective is the analysis of water balance mechanisms by investigating the contribution of fog and the role of forest age in the water cycle. The methods include advanced multitask learning with statistical modelling techniques. The results shown that interception plays a dominant role in the precipitation and evapotranspiration partitioning, enhanced by lichens. Trees transpiration as lower in the young stand and the evapotranspiration of soil and understory contributed considerably to the water balance at both stands. Moreover, fog caused additional throughfall in mixed fog and rain precipitation. RÉSUMÉ: Apprentissage multitâche avec paramétrisation statistique pour une analyse écohydrologique. Cette étude examine les effets des lichens et du brouillard sur le bilan hydrique des Alpes. L'objectif est l'analyse des mécanismes du bilan hydrique: contribution du brouillard et rôle de l'âge de la forêt dans le cycle de l'eau. Les méthodes incluent un apprentissage multitâche avancé avec des techniques de modélisation statistique. Les résultats montrent que l'interception joue un rôle dans la précipitation et l'évapotranspiration, renforcée par de lichens. La transpiration des arbres était plus faible dans le peuplement jeune, tandis que l'évapotranspiration du sol et du sous-bois a contribué considérablement au bilan hydrique des deux peuplements. REZUMAT: Învățare multisarcină cu parametrizare statistică pentru o analiză ecohidrologică. Acest studiu investighează efectele lichenilor și a ceții asupra echilibrului apei din Alpii. Obiectivul este analiza mecanismelor de bilanț al apei: contribuția ceții și rolul vârstei pădurilor în ciclul apei. Metodele includ învățare multisarcină avansată cu tehnici de modelare statistică. Rezultatele au arătat că interceptarea joacă un rol în compartimentarea precipitațiilor și evapotranspirației, sporită de licheni. Transpirația arborilor a fost mai scăzută în arboretul tânăr, iar evapotranspirația solului și a tufărișurilor a contribuit considerabil la echilibrul hidric la ambele arborete. Ceața a provocat căderi suplimentare de precipitații mixte de ceață și ploaie.

M2M (Machine-to-Machine) technologies provide a number of solutions in logistics and transport, such as fleet management solutions, asset tracking systems, parking space management and payment, road tolls, traffic volume monitoring, connected road signs, traffic lights and cameras, connected navigation, passenger services, etc. The data obtained is huge and need high performance to be usable. NoSQL data systems are the answer to it. When compared to relational databases, many NoSQL systems share several key characteristics including a more flexible data model, higher scalability, and a superior performance. Although there are dozens of NoSQL databases, they primarily fall into one of the following three categories: document databases, graph databases and databases based on key-value and wide column model. MongoDB is an open-source document database. In this paper the possibilities of using MongoDB databases in innovative applications of Big Data in the transportation industry will be explored.

Big theory requires more technique to transfer huge amount of data daily. The requirement of this technique is to manage this transfer of data from source to destination. But in big data environment data from different sources are of different formats and we have some technique to manage these data and handle such situation. Many data warehousing and data management approaches have been supported by integration tools such tool as ETL-Extracts, Transform and Load. Extract, transform and load are three database function combined in one place to another. ETL comes from data warehouse but data warehouse technique is not handling or manages the big data integration. ETL-Extract, Transform and load performs the process of transferring the data from the source to destination and manage the information of data.  Extract -The task of extract is to collect the data from external source understand and assess the quality of data. The purpose of extract is to understand the format of data, assess the overall quality of the data and to extract the data from its source so that it can be manipulated in next task.  Transform-The purpose of transform the source format into destination data format understanding the source format and assess the destination format.  Load-Store the data into target system, understand the target system and access the fault tolerance.  Data warehouse is process of transforming all data format into a single format. Date warehouse is manually coded integration programs using java map reduce and Hadoop's. Data are generated from different type of social networking sites, web server, e-mail, web browser etc. all are in unstructured form. To solve these problem organization are try to find out different types of new technologies. Big integration of data has big high volume, high velocity, high variety, high veracity and high value of information.  Volume-Volume is the most important feature of big data theory which adds some additional technologies and tool. Volume contains not only the large amount of data but also the number of sources. Volume has some features as scale, size, amount for data processes and stored in files or database.  Velocity-Big data is generated at high velocity also data generated from different events, arrays sensors in real time.  Variety-Data variety include new requirement of data storage and data format. Variety deals with the complexity of big data and information.  Veracity-Data veracity has security that data must be trusted, original, secure from unauthorized attack. During the whole life cycle data must be secured from trusted source to trusted compute and stored in a trusted and protected storage.  Value-Value is an important feature of data integration defined as added value that collected from different source.

Intelligent systems in automobiles need to be aware of the driving and driver context. Available sensor data stream has to be modeled and monitored in order to do so. Currently there exist no building blocks for hierarchical modeling of driving. By semi-supervised segmentation such building blocks can be discovered. We call them drivemes in analogy to phonemes. More parsimonious modeling of driving becomes now possible.

Maintenance processes of repairable systems have been extensively studied in the past. The resulting simple solutions have proven to be remarkably effective. It requires complex and time-consuming simulations to improve on those simple solutions, and reliable input data is even harder to get. However, new technologies, epitomized by Big Data and the Internet of Things, change the data-availability part of the equation. As a result, there are new exciting possibilities for modeling more subtle effects, and developing processes for easily (and therefore frequently) updated inputs. Modeling decisions can be repeatedly tested on the data, and the models can be quickly adjusted to better reflect reality and even to compensate for missing pieces of the data. In this context, the transparency and simplicity of models becomes a larger virtue. Several examples of the insights based on real-world large-scale applications of predictive analytics using simulation are discussed.

The high Alpine region of northern Italy is characterized by unique ecosystems, a complex hydrogeological setting, steep topographic gradients, variety of vegetation types and landscape patches, and varied in climatic and meteorological factors. Alpine ecosystem is even more complex when the vegetation composition is dominated by coniferous trees, since underground flow conditions and directions have unpredictable water quantities. Modelling such ecosystems requires advanced tools of programming and computing approaches, such as Python. This article is focused on the distributed water balance modelling in alpine catchments. The area is dominated by the coniferous forests (spruce, pine) with trees of different age (old >200 years and young, <30 years). Selected trees are covered by epyhytes (lichens). For effective planning and management of the use of water resources, Python-supported estimations and statistical modelling are a necessary approach for environmental forest monitoring. In particular, the highest suitable spatial resolution that can be achieved in water balance estimations is evaluated in a complicated topographical setting of South Tyrolean Alps with limited knowledge of physiographic factors of forest and meteorological variables (precipitation, temperature, air humidity).

Pengelolaan data historis dalam sistem data warehouse menjadi kebutuhan utama dalam organisasi modern, khususnya untuk analisis jangka panjang. Salah satu pendekatan yang digunakan adalah Slowly Changing Dimension (SCD), yang terbagi menjadi tiga tipe utama: Type 1 (overwrite tanpa histori), Type 2 (penambahan versi dengan histori lengkap), dan Type 3 (penyimpanan nilai histori terbatas).

Penelitian ini mengimplementasikan ketiga tipe tersebut dalam lingkungan sistem manajemen basis data MySQL menggunakan phpMyAdmin sebagai antarmuka. Data percobaan mencakup dimensi karyawan, barang, dan diskon dengan berbagai logika validasi dan transformasi data yang disesuaikan. Hasil implementasi menunjukkan efektivitas masing-masing metode dalam menangani jenis perubahan data tertentu.

Selain menjelaskan struktur SQL dan logika pembaruan, penelitian ini juga menyertakan prosedur tersimpan (stored procedure) untuk mengelola perubahan histori secara otomatis pada SCD Type 2. Studi ini bertujuan memberikan dasar praktis dan konseptual bagi mahasiswa dan praktisi dalam menerapkan SCD untuk data warehouse skala kecil hingga menengah.

A structured conceptual design of reporting systems is a crucial task that has to precede implementation and monitoring. A basic challenge can be seen in information requirements engineering. On the one hand, users need certain information to successfully accomplish their tasks. On the other hand, it has to be avoided to supply them with too much and potentially irrelevant information. Information requirements engineering has to support the conceptual specification of information requirements. Thus, there is need for conceptual languages and appropriate procedure models. We propose a procedure model for the analysis and conceptual design of reporting systems as well as a conceptual modelling language that can be used for both as-is analysis and to-be modelling. It has been developed in an iterative approach based on multiple case studies. Due to the fact that different projects and different basic conditions require the use of different approaches of information requirements engineering, within the procedure model different approaches can be used. In this paper we introduce both procedure model and conceptual modelling language and present findings of a case study in which they were used.

Accurate wind speed forecasting is of great importance for many economic, business and management sectors. This paper introduces a new model based on convolutional neural networks (CNNs) for wind speed prediction tasks. In particular, we show that compared to classical CNN-based models, the proposed model is able to better characterise the spatiotemporal evolution of the wind data by learning the underlying complex input-output relationships from multiple dimensions (views) of the input data. The proposed model exploits the spatio-temporal multivariate multidimensional historical weather data for learning new representations used for wind forecasting. We conduct experiments on two real-life weather datasets. The datasets are measurements from cities in Denmark and in the Netherlands. The proposed model is compared with traditional 2-and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a 2D-CNN model equipped with upscaling and depthwise separable convolutions.

Accurate wind speed forecasting is of great importance for many economic, business and management sectors. This paper introduces a new model based on convolutional neural networks (CNNs) for wind speed prediction tasks. In particular, we show that compared to classical CNN-based models, the proposed model is able to better characterise the spatiotemporal evolution of the wind data by learning the underlying complex input-output relationships from multiple dimensions (views) of the input data. The proposed model exploits the spatio-temporal multivariate multidimensional historical weather data for learning new representations used for wind forecasting. We conduct experiments on two real-life weather datasets. The datasets are measurements from cities in Denmark and in the Netherlands. The proposed model is compared with traditional 2-and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a 2D-CNN model equipped with upscaling and depthwise separable convolutions.