Search results for: missing data imputation

Authors: X. Gumara Rigol, I. Martínez de Apellaniz Anzuola, A. Garcia Serrano, A. Franzi Cros, O. Vidal Calbet, A. Al Maruf

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Traditional federated architectures for data warehousing work well when corporations have existing regional data warehouses and there is a need to aggregate data at a global level. Schibsted Media Group has been maturing from a decentralised organisation into a more globalised one and needed to build both some of the regional data warehouses for some brands at the same time as the global one. In this paper, we present the architectural alternatives studied and why a custom federated approach was the notable recommendation to go further with the implementation. Although the data warehouses are logically federated, the implementation uses a single database system which presented many advantages like: cost reduction and improved data access to global users allowing consumers of the data to have a common data model for detailed analysis across different geographies and a flexible layer for local specific needs in the same place.

Keywords: data integration, data warehousing, federated architecture, Online Analytical Processing (OLAP)

Procedia PDF Downloads 211

Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

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Many quality models have been used to measure e-government portals quality. However, the absence of an international consensus for e-government portals quality models results in many differences in terms of quality attributes and measures. The aim of this paper is to compare and analyze the existing e-government quality models proposed in literature (those that are based on ISO standards and those that are not) in order to propose guidelines to build a good and useful e-government portals quality model. Our findings show that, there is no e-government portal quality model based on the new international standard ISO 25010. Besides that, the quality models are not based on a best practice model to allow agencies to both; measure e-government portals quality and identify missing best practices for those portals.

Keywords: e-government, portal, best practices, quality model, ISO, standard, ISO 25010, ISO 9126

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 202

Authors: Sikander Singh Cheema, Jasmeen Kaur

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In this paper, the concept of data mining is summarized and its one of the important process i.e KDD is summarized. The data mining based on Genetic Algorithm is researched in and ways to achieve the data mining Genetic Algorithm are surveyed. This paper also conducts a formal review on the area of data mining tasks and genetic algorithm in various fields.

Keywords: data mining, KDD, genetic algorithm, descriptive mining, predictive mining

Procedia PDF Downloads 564

Authors: Seung-Lock Seo

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This work presents a data-mining empirical monitoring scheme for industrial processes with partially unbalanced data. Measurement data of good operations are relatively easy to gather, but in unusual special events or faults it is generally difficult to collect process information or almost impossible to analyze some noisy data of industrial processes. At this time some noise filtering techniques can be used to enhance process monitoring performance in a real-time basis. In addition, pre-processing of raw process data is helpful to eliminate unwanted variation of industrial process data. In this work, the performance of various monitoring schemes was tested and demonstrated for discrete batch process data. It showed that the monitoring performance was improved significantly in terms of monitoring success rate of given process faults.

Keywords: data mining, process data, monitoring, safety, industrial processes

Procedia PDF Downloads 364

Authors: Chaimaa Messaoudi, Rachida Fissoune, Hassan Badir

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Technological advances of computer science and data analysis are helping to provide continuously huge volumes of biological data, which are available on the web. Such advances involve and require powerful techniques for data integration to extract pertinent knowledge and information for a specific question. Biomedical exploration of these big data often requires the use of complex queries across multiple autonomous, heterogeneous and distributed data sources. Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontology. We provide a survey of some approaches and techniques for integrating biological data, we focus on those developed in the ontology community.

Keywords: biological ontology, linked data, semantic data integration, semantic web

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Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

Procedia PDF Downloads 83

Authors: Lakshmi Prayaga, Chandra Prayaga. Aaron Wade, Gopi Shankar Mallu, Harsha Satya Pola

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Synthetic data generated by Generative Adversarial Networks and Autoencoders is becoming more common to combat the problem of insufficient data for research purposes. However, generating synthetic data is a tedious task requiring extensive mathematical and programming background. Open-source platforms such as the Synthetic Data Vault (SDV) and Mostly AI have offered a platform that is user-friendly and accessible to non-technical professionals to generate synthetic data to augment existing data for further analysis. The SDV also provides for additions to the generic GAN, such as the Gaussian copula. We present the results from two synthetic data sets (CTGAN data and CTGAN with Gaussian Copula) generated by the SDV and report the findings. The results indicate that the ROC and AUC curves for the data generated by adding the layer of Gaussian copula are much higher than the data generated by the CTGAN.

Keywords: synthetic data generation, generative adversarial networks, conditional tabular GAN, Gaussian copula

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Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

Procedia PDF Downloads 294

Authors: Aylin W Sahin, Alice Jaeger, Laura Nyhan, Gregory Belt, Steffen Münch, Elke K. Arendt

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Our current food systems cause an increase in malnutrition resulting in more people being overweight or obese in the Western World. Additionally, our natural resources are under enormous pressure and the greenhouse gas emission increases yearly with a significant contribution to climate change. Hence, transforming our food systems is of highest priority. Plant-based food products have a lower environmental impact compared to their animal-based counterpart, representing a more sustainable protein source. However, most plant-based protein ingredients, such as soy and pea, are lacking indispensable amino acids and extremely limited in their functionality and, thus, in their food application potential. They are known to have a low solubility in water and change their properties during processing. The low solubility displays the biggest challenge in the development of milk alternatives leading to inferior protein content and protein quality in dairy alternatives on the market. Moreover, plant-based protein ingredients often possess an off-flavour, which makes them less attractive to consumers. EverPro, a plant-protein isolate originated from Brewer’s Spent Grain, the most abundant by-product in the brewing industry, represents the missing piece in the plant protein portfolio. With a protein content of >85%, it is of high nutritional value, including all indispensable amino acids which allows closing the protein quality gap of plant proteins. Moreover, it possesses high techno-functional properties. It is fully soluble in water (101.7 ± 2.9%), has a high fat absorption capacity (182.4 ± 1.9%), and a foaming capacity which is superior to soy protein or pea protein. This makes EverPro suitable for a vast range of food applications. Furthermore, it does not cause changes in viscosity during heating and cooling of dispersions, such as beverages. Besides its outstanding nutritional and functional characteristics, the production of EverPro has a much lower environmental impact compared to dairy or other plant protein ingredients. Life cycle assessment analysis showed that EverPro has the lowest impact on global warming compared to soy protein isolate, pea protein isolate, whey protein isolate, and egg white powder. It also contributes significantly less to freshwater eutrophication, marine eutrophication and land use compared the protein sources mentioned above. EverPro is the prime example of sustainable ingredients, and the type of plant protein the food industry was waiting for: nutritious, multi-functional, and environmentally friendly.

Keywords: plant-based protein, upcycled, brewers' spent grain, low environmental impact, highly functional ingredient

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Authors: Feng Tao, Ma Jing, Guo Xian, Wang Jing

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Named Data Networking (NDN) replaces IP address of traditional network with data name, and adopts dynamic cache mechanism. In the existing mechanism, however, only one-to-one search can be achieved because every data has a unique name corresponding to it. There is a certain mapping relationship between data content and data name, so if the data name is intercepted by an adversary, the privacy of the data content and user’s interest can hardly be guaranteed. In order to solve this problem, this paper proposes a one-to-many fuzzy search scheme based on order-preserving encryption to reduce the query overhead by optimizing the caching strategy. In this scheme, we use hash value to ensure the user’s query safe from each node in the process of search, so does the privacy of the requiring data content.

Keywords: NDN, order-preserving encryption, fuzzy search, privacy

Procedia PDF Downloads 446

Authors: Chellammal Surianarayanan

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Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Roger-Claude Liwanga

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Over the last seven decades, there has been a proliferation of international tribunals. Yet, they have not received unanimous approval, raising a question about their legitimacy. A legitimate international tribunal is one whose authority to adjudicate international disputes is perceived as justified. Using the case study of the International Court of Justice (ICJ), this article highlights the three criteria that should be considered in assessing the legitimacy of an international tribunal, which include legal, sociological, and moral elements. It also contends that the ICJ cannot claim 'full' legitimacy if any of these components of legitimacy is missing in its decisions. The article further suggests that the legitimacy of the ICJ has a dynamic nature, as litigating parties may constantly change their perception of the court’s authority at any time before, during, or after the judicial process. The article equally describes other factors that can contribute to maintaining the international court’s legitimacy, including fairness and unbiasedness, sound interpretation of international legal norms, and transparency.

Keywords: international tribunals, legitimacy, human rights, international law

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Authors: Zakieh Piri, Shahla Damanabi, Peyman Rezaii Hachesoo

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Introduction: Healthcare organizations like other organizations suffer from a number of disorders such as Business Sponsor Disorder, Business Acceptance Disorder, Cultural/Political Disorder, Data Disorder, etc. As quality in healthcare care mostly depends on the quality of data, we aimed to identify data disorders and its symptoms in two teaching hospitals. Methods: Using a self-constructed questionnaire, we asked 20 questions in related to quality and usability of patient data stored in patient records. Research population consisted of 150 managers, physicians, nurses, medical record staff who were working at the time of study. We also asked their views about the symptoms and treatments for any data disorders they mentioned in the questionnaire. Using qualitative methods we analyzed the answers. Results: After classifying the answers, we found six main data disorders: incomplete data, missed data, late data, blurred data, manipulated data, illegible data. The majority of participants believed in their important roles in treatment of data disorders while others believed in health system problems. Discussion: As clinicians have important roles in producing of data, they can easily identify symptoms and disorders of patient data. Health information managers can also play important roles in early detection of data disorders by proactively monitoring and periodic check-ups of data.

Keywords: data disorders, quality, healthcare, treatment

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Authors: Byron Joseph A. Hallar, Annjeannette Alain D. Galang, Maria Visitacion N. Gumabay

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One of the unique challenges provided by the twenty-first century to Philippine higher education is the utilization of Big Data. The higher education system in the Philippines is generating burgeoning amounts of data that contains relevant data that can be used to generate the information and knowledge needed for accurate data-driven decision making. This study examines the status, relevance and future of Big Data and Analytics in Philippine higher education. The insights gained from the study may be relevant to other developing nations similarly situated as the Philippines.

Keywords: big data, data analytics, higher education, republic of the philippines, assessment

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Authors: Rigas Kotsakis, Nikolaos Tsigilis, Vasilis Grammatikopoulos, Evridiki Zachopoulou

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During the last decade, Greece has faced an unprecedented financial crisis, affecting various aspects and functionalities of Higher Education. Besides the restricted funding of academic institutions, the students and their families encountered economical difficulties that undoubtedly influenced the effective completion of their studies. In this context, a fairly large number of students in Alexander campus of International Hellenic University (IHU) delay, interrupt, or even abandon their studies, especially when they come from low-income families, belong to sensitive social or special needs groups, they have different cultural origins, etc. For this reason, a European project, named “Umbrella”, was initiated aiming at providing the necessary psychological support and counseling, especially to disadvantaged students, towards the completion of their studies. To this end, a network of various academic members (academic staff and students) from IHU, namely iMentor, were implicated in different roles. Specifically, experienced academic staff trained students to serve as intermediate links for the integration and educational support of students that fall into the aforementioned sensitive social groups and face problems for the completion of their studies. The main idea of the project is held upon its person-centered character, which facilitates direct student-to-student communication without the intervention of the teaching staff. The backbone of the iMentors network are senior students that face no problem in their academic life and volunteered for this project. It should be noted that there is a provision from the Umbrella structure for substantial and ethical rewards for their engagement. In this context, a well-defined, stringent methodology was implemented for the evaluation of the extent of the problem in IHU and the detection of the profile of the “candidate” disadvantaged students. The first phase included two steps, (a) data collection and (b) data cleansing/ preprocessing. The first step involved the data collection process from the Secretary Services of all Schools in IHU, from 1980 to 2019, which resulted in 96.418 records. The data set included the School name, the semester of studies, a student enrolling criteria, the nationality, the graduation year or the current, up-to-date academic state (still studying, delayed, dropped off, etc.). The second step of the employed methodology involved the data cleansing/preprocessing because of the existence of “noisy” data, missing and erroneous values, etc. Furthermore, several assumptions and grouping actions were imposed to achieve data homogeneity and an easy-to-interpret subsequent statistical analysis. Specifically, the duration of 40 years recording was limited to the last 15 years (2004-2019). In 2004 the Greek Technological Institutions were evolved into Higher Education Universities, leading into a stable and unified frame of graduate studies. In addition, the data concerning active students were excluded from the analysis since the initial processing effort was focused on the detection of factors/variables that differentiated graduate and deleted students. The final working dataset included 21.432 records with only two categories of students, those that have a degree and those who abandoned their studies. Findings of the first phase are presented across faculties and further discussed.

Keywords: higher education, students support, economic crisis, mentoring

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Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

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Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: data management, analytics, energy data analytics, smart grid, smart utilities

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Authors: P. Srinivasa Rao, K. Venkatesh Sharma, G. Sadhya Devi, V. Nagesh

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Privacy Preserving Data Publication is the main concern in present days because the data being published through the internet has been increasing day by day. This huge amount of data was named as Big Data by its size. This project deals the privacy preservation in the context of Big Data using a data warehousing solution called hive. We implemented Nearest Similarity Based Clustering (NSB) with Bottom-up generalization to achieve (v,l)-anonymity. (v,l)-Anonymity deals with the sensitivity vulnerabilities and ensures the individual privacy. We also calculate the sensitivity levels by simple comparison method using the index values, by classifying the different levels of sensitivity. The experiments were carried out on the hive environment to verify the efficiency of algorithms with Big Data. This framework also supports the execution of existing algorithms without any changes. The model in the paper outperforms than existing models.

Keywords: sensitivity, sensitive level, clustering, Privacy Preserving Data Publication (PPDP), bottom-up generalization, Big Data

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Authors: Behnam Tavakkol

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Uncertain data mining algorithms use different ways to consider uncertainty in data such as by representing a data object as a sample of points or a probability distribution. Fuzzy methods have long been used for clustering traditional (certain) data objects. They are used to produce non-crisp cluster labels. For uncertain data, however, besides some uncertain fuzzy k-medoids algorithms, not many other fuzzy clustering methods have been developed. In this work, we develop a fuzzy kernel k-medoids algorithm for clustering uncertain data objects. The developed fuzzy kernel k-medoids algorithm is superior to existing fuzzy k-medoids algorithms in clustering data sets with non-linearly separable clusters.

Keywords: clustering algorithm, fuzzy methods, kernel k-medoids, uncertain data

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Authors: Nirmala Gopal, Sheetal Bhoola, Audecious Mugwagwa

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This paper discusses the continued infringement and exploitation of data by non-state actors for destructive purposes, emphasizing radical terrorist organizations. It will discuss how terrorist organizations access and use data to foster their nefarious agendas. It further examines how cybersecurity, designed as a tool to curb data exploitation, is ineffective in raising global citizens' concerns about how their data can be kept safe and used for its acquired purpose. The study interrogates several policies and data protection instruments, such as the Data Protection Act, Cyber Security Policies, Protection of Personal Information(PPI) and General Data Protection Regulations (GDPR), to understand data use and storage in democratic states. The study outcomes point to the fact that international cybersecurity and cybercrime legislation, policies, and conventions have not curbed violations of data access and use by radical terrorist groups. The study recommends ways to enhance cybersecurity and reduce cyber risks using democratic principles.

Keywords: cybersecurity, data exploitation, terrorist organizations, data democracy

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Authors: Eugenia Jilinguirian

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In the healthcare industry, data mining is essential since it transforms the field by collecting useful data from large datasets. Data mining is the process of applying advanced analytical methods to large patient records and medical histories in order to identify patterns, correlations, and trends. Healthcare professionals can improve diagnosis accuracy, uncover hidden linkages, and predict disease outcomes by carefully examining these statistics. Additionally, data mining supports personalized medicine by personalizing treatment according to the unique attributes of each patient. This proactive strategy helps allocate resources more efficiently, enhances patient care, and streamlines operations. However, to effectively apply data mining, however, and ensure the use of private healthcare information, issues like data privacy and security must be carefully considered. Data mining continues to be vital for searching for more effective, efficient, and individualized healthcare solutions as technology evolves.

Keywords: data mining, healthcare, big data, individualised healthcare, healthcare solutions, database

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Authors: Yunus Doğan, Ahmet Durap

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Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

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Authors: Teris Cheung, Joyce Yuen Ting Lam, Kwan Hin Fong, Yuen Shan Ho, Tim Man Ho Li, Andy Choi-Yeung Tse, Cheng-Ta Li, Calvin Pak-Wing Cheng, Roland Beisteiner

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Transcranial pulse stimulation (TPS) is a non-intrusive brain stimulation technology that has been proven effective in older adults with mild neurocognitive disorders and adults with major depressive disorder. Given these robust evidences, TPS might be an adjunct treatment options in neuropsychiatric disorders, for example, autism spectrum disorder (ASD) – which is a common neurodevelopmental disorder in children. This trial aimed to investigate the effects of TPS on right temporoparietal junction, a key node for social cognition for Autism Spectrum Disorder (ASD), and to examine the association between TPS, executive functions and social functions. Design: This trial adopted a two-armed (verum TPS group vs. sham TPS group), double-blinded, randomized, sham-controlled design. Sampling: 32 subjects aged between 12 and 17, diagnosed with ASD were recruited. All subjects were computerized randomized into either verum TPS group or the sham TPS group on a 1:1 ratio. All subjects undertook functional MRI before and after the TPS interventions. Intervention: Six 30-min TPS sessions were administered to subjects in 2 weeks’ time on alternate days assessing neural connectivity changes. Baseline measurements and post-TPS evaluation of the ASD symptoms, executive functions, and social functions were conducted. Participants were followed up at 2-weeks, at 1-month and 3-month, assessing the short-and long-term sustainability of the TPS intervention. Data analysis: Generalized Estimating Equations with repeated measures were used to analyze the group and time difference. Missing data were managed by multiple imputations. The level of significance was set at p < 0.05. To our best knowledge, this is the first study evaluating the efficacy and safety of TPS among adolescents with ASD in Hong Kong and nationwide. Results emerging from this study will develop insight on whether TPS can be used as an adjunct treatment on ASD in neuroscience and clinical psychiatry. Clinical Trial Registration: ClinicalTrials.gov, identifier: NCT05408793.

Keywords: adolescents, autism spectrum disorder, neuromodulation, rct, transcranial pulse stimulation

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Authors: Anny Retnowati, Elisabeth Sundari

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This research aims to elaborate the meaning of personal data protection principles on patient access to health data in medical records in Indonesia and its implications. The method uses normative legal research by examining health law in Indonesia regarding the patient's right to access their health data in medical records. The data will be analysed qualitatively using the interpretation method to elaborate on the limitation of the meaning of personal data protection principles on patients' access to their data in medical records. The results show that patients only have the right to obtain copies of their health data in medical records. There is no right to inspect directly at any time. Indonesian health law limits the principle of patients' right to broad access to their health data in medical records. This restriction has implications for the reduction of personal data protection as part of human rights. This research contribute to show that a limitaion of personal data protection may abuse the human rights.

Keywords: access, health data, medical records, personal data, protection

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Authors: Martin Böhmer, Agatha Dabrowski, Boris Otto

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The trend of digitization significantly changes the role of data for enterprises. Data turn from an enabler to an intangible organizational asset that requires management and qualifies as a tradeable good. The idea of a networked economy has gained momentum in the data domain as collaborative approaches for data management emerge. Traditional organizational knowledge consequently needs to be extended by comprehensive knowledge about data. The knowledge about data is vital for organizations to ensure that data quality requirements are met and data can be effectively utilized and sovereignly governed. As this specific knowledge has been paid little attention to so far by academics, the aim of the research presented in this paper is to conceptualize it by proposing a “data knowledge model”. Relevant model entities have been identified based on a design science research (DSR) approach that iteratively integrates insights of various industry case studies and literature research.

Keywords: data management, digitization, industry 4.0, knowledge engineering, metamodel

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Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: A. Ibovi Mouondayi, S. Zaher, R. Assadi, K. Erraoui, S. Sboul, J. Daoudim, S. Bousselham, K. Nassar, S. Janani

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INTRODUCTION: Neuropathic pain (NP) caused by damage to the somatosensory nervous system has a significant impact on quality of life and is associated with a high economic burden on the individual and society. The treatment of neuropathic pain consists of the use of a wide range of therapeutic agents, including gabapentin, which is used in the treatment of neuropathic pain. OBJECTIF: The objective of this study was to evaluate the efficacy and tolerance of gabapentin in the treatment of neuropathic pain. MATERIAL AND METHOD: This is a monocentric, cross-sectional, descriptive, retrospective study conducted in our department over a period of 19 months from October 2020 to April 2022. The missing parameters were collected during phone calls of the patients concerned. The diagnostic tool adopted was the DN4 questionnaire in the dialectal Arabic version. The impact of NP was assessed by the visual analog scale (VAS) on pain, sleep, and function. The impact of PN on mood was assessed by the "Hospital anxiety, and depression scale HAD" score in the validated Arabic version. The exclusion criteria were patients followed up for depression and other psychiatric pathologies. RESULTS: A total of 67 patients' data were collected. The average age was 64 years (+/- 15 years), with extremes ranging from 26 years to 94 years. 58 women and 9 men with an M/F sex ratio of 0.15. Cervical radiculopathy was found in 21% of this population, and lumbosacral radiculopathy in 61%. Gabapentin was introduced in doses ranging from 300 to 1800 mg per day with an average dose of 864 mg (+/- 346) per day for an average duration of 12.6 months. Before treatment, 93% of patients had a non-restorative sleep quality (VAS>3). 54% of patients had a pain VAS greater than 5. The function was normal in only 9% of patients. The mean anxiety score was 3.25 (standard deviation: 2.70), and the mean HAD depression score was 3.79 (standard deviation: 1.79). After treatment, all patients had improved the quality of their sleep (p<0.0001). A significant difference was noted in pain VAS, function, as well as anxiety and depression, and HAD score. Gabapentin was stopped for side effects (dizziness and drowsiness) and/or unsatisfactory response. CONCLUSION: Our data demonstrate a favorable effect of gabapentin on the management of neuropathic pain with a significant difference before and after treatment on the quality of life of patients associated with an acceptable tolerance profile.

Keywords: neuropathic pain, chronic pain, treatment, gabapentin

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Authors: Jeroen S. de Bruin, Karin Schindler, Christian Schuh

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With increasingly more mobile health applications appearing due to the popularity of smartphones, the possibility arises that these data can be used to improve the medical diagnostic process, as well as the overall quality of healthcare, while at the same time lowering costs. However, as of yet there have been no reports of a successful combination of patient-generated data from smartphones with data from clinical routine. In this paper, we describe how these two types of data can be combined in a secure way without modification to hospital information systems, and how they can together be used in a medical expert system for automatic nutritional classification and triage.

Keywords: mobile health, data integration, expert systems, disease-related malnutrition

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Authors: Casta Guillaume, Robert Jagers, Deborah Rivas-Drake

Abstract:

Civically engaged youth are critical to maintaining and/or improving the functioning of local, national and global communities and their institutions. The present study investigated how school climate and academic beliefs (academic self-efficacy and school belonging) may inform emergent civic behaviors (emergent citizenship) among self-identified middle school youth of color (African American, Multiracial or Mixed, Latino, Asian American or Pacific Islander, Native American, and other). Study aims: 1) Understand whether and how school climate is associated with civic engagement behaviors, directly and indirectly, by fostering a positive sense of connection to the school and/or engendering feelings of self-efficacy in the academic domain. Accordingly, we examined 2) The association of youths’ sense of school connection and academic self-efficacy with their personally responsible and participatory civic behaviors in school and community contexts—both concurrently and longitudinally. Data from two subsamples of a larger study of social/emotional development among middle school students were used for longitudinal and cross sectional analysis. The cross-sectional sample included 324 6th-8th grade students, of which 43% identified as African American, 20% identified as Multiracial or Mixed, 18% identified as Latino, 12% identified as Asian American or Pacific Islander, 6% identified as Other, and 1% identified as Native American. The age of the sample ranged from 11 – 15 (M = 12.33, SD = .97). For the longitudinal test of our mediation model, we drew on data from the 6th and 7th grade cohorts only (n =232); the ethnic and racial diversity of this longitudinal subsample was virtually identical to that of the cross-sectional sample. For both the cross-sectional and longitudinal analyses, full information maximum likelihood was used to deal with missing data. Fit indices were inspected to determine if they met the recommended thresholds of RMSEA below .05 and CFI and TLI values of at least .90. To determine if particular mediation pathways were significant, the bias-corrected bootstrap confidence intervals for each indirect pathway were inspected. Fit indices for the latent variable mediation model using the cross-sectional data suggest that the hypothesized model fit the observed data well (CFI = .93; TLI =. 92; RMSEA = .05, 90% CI = [.04, .06]). In the model, students’ perceptions of school climate were significantly and positively associated with greater feelings of school connectedness, which were in turn significantly and positively associated with civic engagement. In addition, school climate was significantly and positively associated with greater academic self-efficacy, but academic self-efficacy was not significantly associated with civic engagement. Tests of mediation indicated there was one significant indirect pathway between school climate and civic engagement behavior. There was an indirect association between school climate and civic engagement via its association with sense of school connectedness, indirect association estimate = .17 [95% CI: .08, .32]. The aforementioned indirect association via school connectedness accounted for 50% (.17/.34) of the total effect. Partial support was found for the prediction that students’ perceptions of a positive school climate are linked to civic engagement in part through their role in students’ sense of connection to school.

Keywords: civic engagement, early adolescence, school climate, school belonging, developmental niche

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Authors: Sombol Mokhles

Abstract:

This paper tries to show the prospects of utilising (big)data for enabling just the transition of diverse cities. Our key purpose is to offer a framework of applications and implications of utlising (big) data in comparing sustainability transitions across different cities. Relying on the cosmopolitan comparison, this paper explains the potential application of (big) data but also its limitations. The paper calls for adopting a data-driven and just perspective in including different cities around the world. Having a just and inclusive approach at the front and centre ensures a just transition with synergistic effects that leave nobody behind.

Keywords: big data, just sustainable transition, cosmopolitan city comparison, cities

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