Search results for: semantic data profiling

Authors: Dhananjay Singh, M. Abdullah-Al-Wadud

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The Internet of Things is a concept of a large scale ecosystem of wireless actuators. The actuators are defined as things in the IoT, those which contribute or produces some data to the ecosystem. However, ubiquitous data collection, data security, privacy preserving, large volume data processing, and intelligent analytics are some of the key challenges into the IoT technologies. In order to solve the security requirements, challenges and threats in the IoT, we have discussed a message authentication mechanism for IoT applications. Finally, we have discussed data encryption mechanism for messages authentication before propagating into IoT networks.

Keywords: Internet of Things (IoT), message authentication, privacy, security

Procedia PDF Downloads 384

Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

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Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

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Authors: Xinge Liao, Francesc Xavier Roige Ventura, Dolores Sanchez Aguilera

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With the development of the Internet, the study of tourism behavior has rapidly expanded from the traditional physical market to the online market. Data on the Internet is characterized by dynamic changes, and new data appear all the time. In recent years the generation of a large volume of data was characterized, such as forums, blogs, and other sources, which have expanded over time and space, together they constitute large-scale Internet data, known as Big Data. This data of technological origin that derives from the use of devices and the activity of multiple users is becoming a source of great importance for the study of geography and the behavior of tourists. The study will focus on cultural heritage tourist practices in the context of Big Data. The research will focus on exploring the characteristics and behavior of Chinese tourists in relation to the cultural heritage of Catalonia. Geographical information, target image, perceptions in user-generated content will be studied through data analysis from Weibo -the largest social networks of blogs in China. Through the analysis of the behavior of heritage tourists in the Big Data environment, this study will understand the practices (activities, motivations, perceptions) of cultural tourists and then understand the needs and preferences of tourists in order to better guide the sustainable development of tourism in heritage sites.

Keywords: Barcelona, Big Data, Catalonia, cultural heritage, Chinese tourism market, tourists’ behavior

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Authors: L. Calcagni, A. Battisti, M. Hensel, D. S. Hensel

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Since the beginning of the 21st century, we have seen a dynamic growth of water-based (WB) architecture, mainly due to the increasing threat of floods caused by sea level rise and heavy rains, all correlated with climate change. At the same time, the shortage of land available for urban development also led architects, engineers, and policymakers to reclaim the seabed or to build floating structures. Furthermore, the drive to produce energy from renewable resources has expanded the sector of offshore research, mining, and energy industry which seeks new types of WB structures. In light of these considerations, the time is ripe to consider floating architecture as a full-fledged building typology. Currently, there is no universally recognized academic definition of a floating building. Research on floating architecture lacks a proper, commonly shared vocabulary and typology distinction. Moreover, there is no global international legal framework for urban development on water, and there is no structured performance based building design (PBBD) approach for floating architecture in most countries, let alone national regulatory systems. Thus, first of all, the research intends to overcome the semantic and typological issues through the conceptualization of floating architecture, laying the proto-ontological conditions for floating development, and secondly to identify the parameters to be considered in the definition of a specific PBBD framework, setting the scene for national planning strategies. The theoretical overview and re-semanticization process involve the attribution of a new meaning to the term floating architecture. This terminological work of semantic redetermination is carried out through a systematic literature review and involves quantitative and historical research as well as logical argumentation methods. As it is expected that floating urban development is most likely to take place as an extension of coastal areas, the needs and design criteria are definitely more similar to those of the urban environment than to those of the offshore industry. Therefore, the identification and categorization of parameters –looking towards the potential formation of a PBBD framework for floating development– takes the urban and architectural guidelines and regulations as the starting point, taking the missing aspects, such as hydrodynamics (i.e. stability and buoyancy) from the offshore and shipping regulatory frameworks. This study is carried out through an evidence-based assessment of regulatory systems that are effective in different countries around the world, addressing on-land and on-water architecture as well as offshore and shipping industries. It involves evidence-based research and logical argumentation methods. Overall, inhabiting water is proposed not only as a viable response to the problem of rising sea levels, thus as a resilient frontier for urban development, but also as a response to energy insecurity, clean water, and food shortages, environmental concerns, and urbanization, in line with Blue Economy principles and the Agenda 2030. This review shows how floating architecture is to all intents and purposes, an urban adaptation measure and a solution towards self-sufficiency and energy-saving objectives. Moreover, the adopted methodology is, to all extents, open to further improvements and integrations, thus not rigid and already completely determined. Along with new designs and functions that will come into play in the practice field, eventually, life on water will seem no more unusual than life on land, especially by virtue of the multiple advantages it provides not only to users but also to the environment.

Keywords: adaptation measures, building typology, floating architecture, performance based building design, rising sea levels

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Authors: Darusalam, Jorish Hulstijn, Marijn Janssen

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Media has revealed a variety of corruption cases in the regional and local governments all over the world. Many governments pursued many anti-corruption reforms and have created a system of checks and balances. Three types of corruption are faced by citizens; administrative corruption, collusion and extortion. Accountability is one of the benchmarks for building transparent government. The public sector is required to report the results of the programs that have been implemented so that the citizen can judge whether the institution has been working such as economical, efficient and effective. Open Data is offering solutions for the implementation of good governance in organizations who want to be more transparent. In addition, Open Data can create transparency and accountability to the community. The objective of this paper is to build a framework of open data for accountability to combating corruption. This paper will investigate the relationship between open data, and accountability as part of anti-corruption initiatives. This research will investigate the impact of open data implementation on public organization.

Keywords: open data, accountability, anti-corruption, framework

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Authors: Anes Enakoa, Yawei Liang

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Question Answering (QA) system is one of the most important and demanding tasks in the field of Natural Language Processing (NLP). In QA systems, the answer generation task generates a list of candidate answers to the user's question, in which only one answer is correct. Answer selection is one of the main components of the QA, which is concerned with selecting the best answer choice from the candidate answers suggested by the system. However, the selection process can be very challenging especially in Arabic due to its particularities. To address this challenge, an approach is proposed to answer questions with multiple answer choices for Arabic QA systems based on Textual Entailment (TE) recognition. The developed approach employs a Support Vector Machine that considers lexical, semantic and syntactic features in order to recognize the entailment between the generated hypotheses (H) and the text (T). A set of experiments has been conducted for performance evaluation and the overall performance of the proposed method reached an accuracy of 67.5% with C@1 score of 80.46%. The obtained results are promising and demonstrate that the proposed method is effective for TE recognition task.

Keywords: information retrieval, machine learning, natural language processing, question answering, textual entailment

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Yuyun Wabula, B. J. Dewancker

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In the past decade, the social networking app has been growing very rapidly. Geolocation data is one of the important features of social media that can attach the user's location coordinate in the real world. This paper proposes the use of geolocation data from the Twitter social media application to gain knowledge about urban dynamics, especially on human mobility behavior. This paper aims to explore the relation between geolocation Twitter with the existence of people in the urban area. Firstly, the study will analyze the spread of people in the particular area, within the city using Twitter social media data. Secondly, we then match and categorize the existing place based on the same individuals visiting. Then, we combine the Twitter data from the tracking result and the questionnaire data to catch the Twitter user profile. To do that, we used the distribution frequency analysis to learn the visitors’ percentage. To validate the hypothesis, we compare it with the local population statistic data and land use mapping released by the city planning department of Makassar local government. The results show that there is the correlation between Twitter geolocation and questionnaire data. Thus, integration the Twitter data and survey data can reveal the profile of the social media users.

Keywords: geolocation, Twitter, distribution analysis, human mobility

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: Sudipto Shanker Dasgupta

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One of the largest mining companies wanted to look at health analytics for their driverless trucks. These trucks were the key to their supply chain logistics. The automated trucks had multi-level sub-assemblies which would send out sensor information. The use case that was worked on was to capture the sensor signal from the truck subcomponents and analyze the health of the trucks from repair and replacement purview. Open source software was used to stream the data into a clustered Hadoop setup in Amazon Web Services cloud and Apache Spark SQL was used to analyze the data. All of this was achieved through a 10 node amazon 32 core, 64 GB RAM setup real-time analytics was achieved on ‘300 million records’. To check the scalability of the system, the cluster was increased to 100 node setup. This talk will highlight how Open Source software was used to achieve the above use case and the insights on the high data throughput on a cloud set up.

Keywords: streaming analytics, data science, big data, Hadoop, high throughput, sensor data

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Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Elizabeta Krstić Vukelja

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Digitization of personal data is a consequence of the development of information and communication technologies that create a new work environment with many advantages and challenges, but also potential threats to privacy and personal data protection. Regulation (EU) 2016/679 of the European Parliament and of the Council is becoming a law and obligation that should address the issues of personal data protection and information security. The existence of the Regulation leads to the conclusion that national legislation in the field of virtual environment, protection of the rights of EU citizens and processing of their personal data is insufficiently effective. In the health system, special emphasis is placed on the processing of special categories of personal data, such as health data. The healthcare industry is recognized as a particularly sensitive area in which a large amount of medical data is processed, the digitization of which enables quick access and quick identification of the health insured. The protection of the individual requires quality IT solutions that guarantee the technical protection of personal categories. However, the real problems are the technical and human nature and the spatial limitations of the application of the Regulation. Some conclusions will be drawn by analyzing the implementation of the basic principles of the Regulation on the example of the Croatian health care system and comparing it with similar activities in other EU member states.

Keywords: regulation, healthcare system, personal dana protection, quality data assurance

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Authors: Nagi Mekhiel

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Many applications use vector operations by applying single instruction to multiple data that map to different locations in conventional memory. Transferring data from memory is limited by access latency and bandwidth affecting the performance gain of vector processing. We present a memory system that makes all of its content available to processors in time so that processors need not to access the memory, we force each location to be available to all processors at a specific time. The data move in different orbits to become available to other processors in higher orbits at different time. We use this memory to apply parallel vector operations to data streams at first orbit level. Data processed in the first level move to upper orbit one data element at a time, allowing a processor in that orbit to apply another vector operation to deal with serial code limitations inherited in all parallel applications and interleaved it with lower level vector operations.

Keywords: Memory Organization, Parallel Processors, Serial Code, Vector Processing

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Tammy Wee, Detlev Remy, Arif Perdana

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In the recent years, data analytics has become the buzzword in the hospitality industry. The hospitality industry is another example of a data-rich industry that has yet fully benefited from the insights of data analytics. Effective use of data analytics can change how hotels operate, market and position themselves competitively in the hospitality industry. However, at the moment, the data obtained by individual hotels remain under-utilized. This research is a preliminary research on data analytics in the hospitality industry, using an in-depth face-to-face interview on one hotel as a start to a multi-level research. The main case study of this research, hotel A, is a chain brand of international hotel that has been systematically gathering and collecting data on its own customer for the past five years. The data collection points begin from the moment a guest book a room until the guest leave the hotel premises, which includes room reservation, spa booking, and catering. Although hotel A has been gathering data intelligence on its customer for some time, they have yet utilized the data to its fullest potential, and they are aware of their limitation as well as the potential of data analytics. Currently, the utilization of data analytics in hotel A is limited in the area of customer service improvement, namely to enhance the personalization of service for each individual customer. Hotel A is able to utilize the data to improve and enhance their service which in turn, encourage repeated customers. According to hotel A, 50% of their guests returned to their hotel, and 70% extended nights because of the personalized service. Apart from using the data analytics for enhancing customer service, hotel A also uses the data in marketing. Hotel A uses the data analytics to predict or forecast the change in consumer behavior and demand, by tracking their guest’s booking preference, payment preference and demand shift between properties. However, hotel A admitted that the data they have been collecting was not fully utilized due to two challenges. The first challenge of using data analytics in hotel A is the data is not clean. At the moment, the data collection of one guest profile is meaningful only for one department in the hotel but meaningless for another department. Cleaning up the data and getting standards correctly for usage by different departments are some of the main concerns of hotel A. The second challenge of using data analytics in hotel A is the non-integral internal system. At the moment, the internal system used by hotel A do not integrate with each other well, limiting the ability to collect data systematically. Hotel A is considering another system to replace the current one for more comprehensive data collection. Hotel proprietors recognized the potential of data analytics as reported in this research, however, the current challenges of implementing a system to collect data come with a cost. This research has identified the current utilization of data analytics and the challenges faced when it comes to implementing data analytics.

Keywords: data analytics, hospitality industry, customer relationship management, hotel marketing

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Authors: Xiaodi Su, Xin Ting Zheng, Laura Sutarlie, Nur Asinah binte Mohamed Salleh, Yong Yu

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Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We have developed paper-like battery-free multiplexed sensors for holistic wound assessment via quantitative detection of multiple inflammation and infection markers. In one of the designs, the sensor patch consists of a wax-printed paper panel with five colorimetric sensor channels arranged in a pattern resembling a five-petaled flower (denoted as a ‘Petal’ sensor). The five sensors are for temperature, pH, trimethylamine, uric acid, and moisture. The sensor patch is sandwiched between a top transparent silicone layer and a bottom adhesive wound contact layer. In the second design, a palm-like-shaped paper strip is fabricated by a paper-cutter printer (denoted as ‘Palm’ sensor). This sensor strip carries five sensor regions connected by a stem sampling entrance that enables rapid colorimetric detection of multiple bacteria metabolites (aldehyde, lactate, moisture, trimethylamine, tryptophan) from wound exudate. For both the “\’ Petal’ and ‘Palm’ sensors, color images can be captured by a mobile phone. According to the color changes, one can quantify the concentration of the biomarkers and then determine wound healing status and identify/quantify bacterial species in infected wounds. The ‘Petal’ and ‘Palm’ sensors are validated with in-situ animal and ex-situ skin wound models, respectively. These sensors have the potential for integration with wound dressing to allow early warning of adverse events without frequent removal of the plasters. Such in-situ and early detection of non-healing condition can trigger immediate clinical intervention to facilitate wound care management.

Keywords: wound infection, colorimetric sensor, paper fluidic sensor, wound care

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Authors: Djelloul Benatiallah, Ali Benatiallah, Abdelkader Harouz

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Geographic Information Systems (GIS) include various methods and computer techniques to model, capture digitally, store, manage, view and analyze. Geographic information systems have the characteristic to appeal to many scientific and technical field, and many methods. In this article we will present a complete and operational geographic information system, following the theoretical principles of data management and adapting to spatial data, especially data concerning the monitoring of drinking water supply wells (DWS) Adrar region. The expected results of this system are firstly an offer consulting standard features, updating and editing beneficiaries and geographical data, on the other hand, provides specific functionality contractors entered data, calculations parameterized and statistics.

Keywords: GIS, DWS, drilling, Adrar

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Authors: S. Habte, K. Ouazzane, P. Patel, S. Patel

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The dynamic and highly competitive nature of the consumer electronics retail industry means that businesses in this industry are experiencing different decision making challenges in relation to pricing, inventory control, consumer satisfaction and product offerings. To overcome the challenges facing retailers and create opportunities, we propose a generic data warehousing solution which can be applied to a wide range of consumer electronics retailers with a minimum configuration. The solution includes a dimensional data model, a template SQL script, a high level architectural descriptions, ETL tool developed using C#, a set of APIs, and data access tools. It has been successfully applied by ASK Outlets Ltd UK resulting in improved productivity and enhanced sales growth.

Keywords: consumer electronics, data warehousing, dimensional data model, generic, retail industry

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Authors: Lei Ren, Michael Hartnett, Stephen Nash

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The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

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Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

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This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values.

Keywords: AASHTOWare, traffic, weigh-in-motion, axle load distribution

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Authors: Angga Hidayah Ramadhan, Luciana Andrawina, M. Azani Hasibuan

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Data will be as a core of the decision if it has a good treatment or process, which is process that data into information, and information into knowledge to make a wisdom or decision. Today, many companies have not realize it include XYZ University Admission Directorate as executor of National Admission called Seleksi Masuk Bersama (SMB) that during the time, the workers only uses their feeling to make a decision. Whereas if it done, then that company can analyze the data to make a right decision to get a pin sales from student candidate or registrant that follow SMB as many as possible. Therefore, needs Knowledge Management System (KMS) with Geographic Information System (GIS) use 5C4C that can process that company data becomes more useful and can help make decisions. This information system can process data into information based on the pin sold data with 5C (Contextualized, Categorize, Calculation, Correction, Condensed) and convert information into knowledge with 4C (Comparing, Consequence, Connection, Conversation) that has been several steps until these data can be useful to make easier to take a decision or wisdom, resolve problems, communicate, and quicker to learn to the employees have not experience and also for ease of viewing/visualization based on spatial data that equipped with GIS functionality that can be used to indicate events in each province with indicator that facilitate in this system. The system also have a function to save the tacit on the system then to be proceed into explicit in expert system based on the problems that will be found from the consequences of information. With the system each team can make a decision with same ways, structured, and the important is based on the actual event/data.

Keywords: 5C4C, data, information, knowledge

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Authors: Shravani Itkelwar, Deepak Tewari, Bhaskar Natarajan

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The energy consumption data plays a vital role in energy efficiency policy design, implementation, and impact assessment. Any demand-side energy management intervention's success relies on the availability of accurate, comprehensive, granular, and up-to-date data on energy consumption. The Building sector, including residential and commercial, is one of the largest consumers of energy in India after the Industrial sector. With economic growth and increasing urbanization, the building sector is projected to grow at an unprecedented rate, resulting in a 5.6 times escalation in energy consumption till 2047 compared to 2017. Therefore, energy efficiency interventions will play a vital role in decoupling the floor area growth and associated energy demand, thereby increasing the need for robust data. In India, multiple institutions are involved in the collection and dissemination of data. This paper focuses on energy consumption data management in the building sector in India for both residential and commercial segments. It evaluates the robustness of data available through administrative and survey routes to estimate the key performance indicators and identify critical data gaps for making informed decisions. The paper explores several issues in the data, such as lack of comprehensiveness, non-availability of disaggregated data, the discrepancy in different data sources, inconsistent building categorization, and others. The identified data gaps are justified with appropriate examples. Moreover, the paper prioritizes required data in order of relevance to policymaking and groups it into "available," "easy to get," and "hard to get" categories. The paper concludes with recommendations to address the data gaps by leveraging digital initiatives, strengthening institutional capacity, institutionalizing exclusive building energy surveys, and standardization of building categorization, among others, to strengthen the management of building sector energy consumption data.

Keywords: energy data, energy policy, energy efficiency, buildings

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Authors: Camilla Marraccini

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The sarcophagus of Adelphia represents an iconographic unicum compared to female representations on sarcophagi. From the time of its discovery in 1872 until recent studies , the figurative program of the sarcophagus' lid has been the subject of extensive debate. Critics have been divided between those who have interpreted the scene as the life of Mary , those who have interpreted it as a depiction of Adelphia , those who saw it as a traditional representation of Moses and Christ , and those who read a particular philosophical doctrine depicting Sophia dei . Indeed, next to the female orans, Maries on the throne and female servants of Christ, the distinctiveness of the sarcophagus becomes evident and a unique scene stands out on the lid: a procession of nine women culminating with a frontal woman on the throne. Using an iconographic analysis that questions 1.the traces of the typologies of christian women found in sarcophagi and 2.the pagan models used by the artisans of Christianity, this paper aims to investigate an interpretation of the scene based on the discoveries of meaning and semantic shifts that can be understood once the model used for this iconography is found, whilst conducting a comparative analysis that underlines the importance of understanding the reasons behind this commission. Searching for the commissioner's will needs to be an interdisciplinary quest: from iconographical analysis to epigraphical and archaeological data. Considering, that several recent studies have emphasized the important female presence in the catacomb of St.Giovanni (perhaps a burial dedicated exclusively to female monastic cults?) , where the sarcophagus was found, the local development of the cult of St. Lucia, and the fact that Adelfia is referred to as clarissima femina in her inscription, the present paper will try to make a transition between art and social context. Thus, this paper aims to understand whether this iconography can be an ulterior source, next to the epigraphical ones, to reconstruct the concrete role of Adelfia in local women's communities. In summary, by tracing different forms of continuity between pagan and christian practices, this paper aims to understand what the relationship between actual hierarchical power, funerary self-representations and the inherited semantics of images could tell us about the life and death of late antique Christian women.

Keywords: early Christian art, female bodies, iconography, female communities, sarcophagi

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Authors: Behnam Jay

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In this study, the phenomenon called “Strengthening” in human language refers to the strategic use of multiple linguistic elements to intensify specific grammatical or semantic functions. This study explores cross-linguistic evidence demonstrating how strengthening appears in various grammatical structures. In French and Spanish, double negatives are used not to cancel each other out but to intensify the negation, challenging the conventional understanding that double negatives result in an affirmation. For example, in French, il ne sait pas (He dosn't know.) uses both “ne” and “pas” to strengthen the negation. Similarly, in Spanish, No vio a nadie. (He didn't see anyone.) uses “no” and “nadie” to achieve a stronger negative meaning. In Japanese, double honorifics, often perceived as erroneous, are reinterpreted as intentional efforts to amplify politeness, as seen in forms like ossharareru (to say, (honorific)). Typically, an honorific morpheme appears only once in a predicate, but native speakers often use double forms to reinforce politeness. In Turkish, the word eğer (indicating a condition) is sometimes used together with the conditional suffix -se(sa) within the same sentence to strengthen the conditional meaning, as in Eğer yağmur yağarsa, o gelmez. (If it rains, he won't come). Furthermore, the combination of question words with rising intonation in various languages serves to enhance interrogative force. These instances suggest that strengthening is a cross-linguistic strategy that may reflect a broader cognitive mechanism in language processing. This paper investigates these cases in detail, providing insights into why languages may adopt such strategies. No corpus was used for collecting examples from different languages. Instead, the examples were gathered from languages the author encountered during their research, focusing on specific grammatical and morphological phenomena relevant to the concept of strengthening. Due to the complexity of employing a comparative method across multiple languages, this approach was chosen to illustrate common patterns of strengthening based on available data. It is acknowledged that different languages may have different strengthening strategies in various linguistic domains. While the primary focus is on grammar and morphology, it is recognized that the strengthening phenomenon may also appear in phonology. Future research should aim to include a broader range of languages and utilize more comprehensive comparative methods where feasible to enhance methodological rigor and explore this phenomenon more thoroughly.

Keywords: strengthening, cross-linguistic analysis, syntax, semantics, cognitive mechanism

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Authors: Silvina Caíno-Lores, Jesús Carretero

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Large scale computing infrastructures have been widely developed with the core objective of providing a suitable platform for high-performance and high-throughput computing. These systems are designed to support resource-intensive and complex applications, which can be found in many scientific and industrial areas. Currently, large scale data-intensive applications are hindered by the high latencies that result from the access to vastly distributed data. Recent works have suggested that improving data locality is key to move towards exascale infrastructures efficiently, as solutions to this problem aim to reduce the bandwidth consumed in data transfers, and the overheads that arise from them. There are several techniques that attempt to move computations closer to the data. In this survey we analyse the different mechanisms that have been proposed to provide data locality for large scale high-performance and high-throughput systems. This survey intends to assist scientific computing community in understanding the various technical aspects and strategies that have been reported in recent literature regarding data locality. As a result, we present an overview of locality-oriented techniques, which are grouped in four main categories: application development, task scheduling, in-memory computing and storage platforms. Finally, the authors include a discussion on future research lines and synergies among the former techniques.

Keywords: data locality, data-centric computing, large scale infrastructures, cloud computing

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Authors: Harold P. Villota, Alejandro Osorio B.

Abstract:

The energy meteorology is an area for study energy complementarity and the use of renewable sources in interconnected systems. Due to diversify the energy matrix in Colombia with wind sources, is necessary to know the data bases about this one. However, the time series given by 260 automatic weather stations have empty, and no apply data, so the purpose is to fill the time series selecting two years to characterize, impute and use like base to complete the data between 2005 and 2020.

Keywords: complementarity, wind speed, renewable, colombia, characteri, characterization, imputation

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Authors: Hyun-Woo Cho

Abstract:

Unexpected events may occur with serious impacts on industrial process. This work utilizes a data representation technique to model and to analyze process data pattern for the purpose of diagnosis. In this work, the use of triangular representation of process data is evaluated using simulation process. Furthermore, the effect of using different pre-treatment techniques based on such as linear or nonlinear reduced spaces was compared. This work extracted the fault pattern in the reduced space, not in the original data space. The results have shown that the non-linear technique based diagnosis method produced more reliable results and outperforms linear method.

Keywords: process monitoring, data analysis, pattern modeling, fault, nonlinear techniques

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Authors: Mostafa Gamal, Hoda K. Mohamed, Islam El-Maddah, Ali Hamdi

Abstract:

In recent years, many digital documents on the web have been created due to the rapid growth of ’social applications’ communities or ’Data-intensive applications’. The evolution of online-based multimedia data poses new challenges in storing and querying large amounts of data for online recommender systems. Graph data models have been shown to be more efficient than relational data models for processing complex data. This paper will explain the key differences between graph and relational databases, their strengths and weaknesses, and why using graph databases is the best technology for building a realtime recommendation system. Also, The paper will discuss several similarity metrics algorithms that can be used to compute a similarity score of pairs of nodes based on their neighbourhoods or their properties. Finally, the paper will discover how NLP strategies offer the premise to improve the accuracy and coverage of realtime recommendations by extracting the information from the stored unstructured knowledge, which makes up the bulk of the world’s data to enrich the graph database with this information. As the size and number of data items are increasing rapidly, the proposed system should meet current and future needs.

Keywords: graph databases, NLP, recommendation systems, similarity metrics

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Authors: Osakwe Jude Odiakaosa

Abstract:

Today’s digital society is characterized by e-education or e-learning, e-commerce, and so on. All these have been propelled by digital revolution. Digital technology such as computer technology, Global Positioning System (GPS) and Geographic Information System (GIS) has been having a tremendous impact on the field of technology. This development has positively affected the scope, methods, speed of data acquisition, data management and the rate of delivery of the results (map and other map products) of data processing. This paper tries to address the impact of revolution brought by digital technology.

Keywords: digital revolution, internet, technology, data management

Procedia PDF Downloads 449