Search results for: data retrieval

Authors: Tamas Jancso, Andrea Podor, Eva Nagyne Hajnal, Peter Udvardy, Gabor Nagy, Attila Varga, Meng Qingyan

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The paper deals with the conditions and circumstances of integration of remotely sensed data for rural environmental monitoring purposes. The main task is to make decisions during the integration process when we have data sources with different resolution, location, spectral channels, and dimension. In order to have exact knowledge about the integration and data fusion possibilities, it is necessary to know the properties (metadata) that characterize the data. The paper explains the joining of these data sources using their attribute data through a sample project. The resulted product will be used for rural environmental analysis.

Keywords: remote sensing, GIS, metadata, integration, environmental analysis

Procedia PDF Downloads 124

Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: Yu-Mi Song, Sung-Ah Kim, Dongyoun Shin

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Cities are complex systems of diverse and inter-tangled activities. These activities and their complex interrelationships create diverse urban phenomena. And such urban phenomena have considerable influences on the lives of citizens. This research aimed to develop a method to reveal the causes and effects among diverse urban elements in order to enable better understanding of urban activities and, therefrom, to make better urban planning strategies. Specifically, this study was conducted to solve a data-recommendation problem found on a Korean public data homepage. First, a correlation analysis was conducted to find the correlations among random urban data. Then, based on the results of that correlation analysis, the weighted data network of each urban data was provided to people. It is expected that the weights of urban data thereby obtained will provide us with insights into cities and show us how diverse urban activities influence each other and induce feedback.

Keywords: big data, machine learning, ontology model, urban data model

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Authors: Abeba Nigussie Turi, Xiangming Samuel Li

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Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.

Keywords: startup data analytics, data-driven decision-making, data acquisition, data generation, digital entrepreneurship

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Authors: Gokulan Vethanayakam, Daniel Aston

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Introduction: The ReSPECT process supports healthcare professionals when making patient-centered decisions in the event of an emergency. It has been widely adopted by the NHS in England and allows patients to express thoughts and wishes about treatments and outcomes that they consider acceptable. It includes (but is not limited to) cardiopulmonary resuscitation decisions. ReSPECT conversations should ideally occur prior to ICU admission and should be documented in the eight sections of the nationally-standardised ReSPECT form. This audit evaluated the use of ReSPECT on a busy cardiothoracic ICU in an NHS Trust where established policies advocating its use exist. Methods: This audit was a retrospective review of ReSPECT forms for a sample of high-risk patients admitted to ICU at the Royal Papworth Hospital between January 2021 and March 2022. Patients all received one of the following interventions: Veno-Venous Extra-Corporeal Membrane Oxygenation (VV-ECMO) for severe respiratory failure (retrieved via the national ECMO service); cardiac or pulmonary transplantation-related surgical procedures (including organ transplants and Ventricular Assist Device (VAD) implantation); or elective non-transplant cardiac surgery. The quality of documentation on ReSPECT forms was evaluated using national standards and a graded ranking tool devised by the authors which was used to assess narrative aspects of the forms. Quality was ranked as A (excellent) to D (poor). Results: Of 230 patients (74 VV-ECMO, 104 transplant, 52 elective non-transplant surgery), 43 (18.7%) had a ReSPECT form and only one (0.43%) patient had a ReSPECT form completed prior to ICU admission. Of the 43 forms completed, 38 (88.4%) were completed due to the commencement of End of Life (EoL) care. No non-transplant surgical patients included in the audit had a ReSPECT form. There was documentation of balance of care (section 4a), CPR status (section 4c), capacity assessment (section 5), and patient involvement in completing the form (section 6a) on all 43 forms. Of the 34 patients assessed as lacking capacity to make decisions, only 22 (64.7%) had reasons documented. Other sections were variably completed; 29 (67.4%) forms had relevant background information included to a good standard (section 2a). Clinical guidance for the patient (section 4b) was given in 25 (58.1%), of which 11 stated the rationale that underpinned it. Seven forms (16.3%) contained information in an inappropriate section. In a comparison of ReSPECT forms completed ahead of an EoL trigger with those completed when EoL care began, there was a higher number of entries in section 3 (considering patient’s values/fears) that were assessed at grades A-B in the former group (p = 0.014), suggesting higher quality. Similarly, forms from the transplant group contained higher quality information in section 3 than those from the VV-ECMO group (p = 0.0005). Conclusions: Utilisation of the ReSPECT process in high-risk patients is yet to be well-adopted in this trust. Teams who meet patients before hospital admission for transplant or high-risk surgery should be encouraged to engage with the ReSPECT process at this point in the patient's journey. VV-ECMO retrieval teams should consider ReSPECT conversations with patients’ relatives at the time of retrieval.

Keywords: audit, critical care, end of life, ICU, ReSPECT, resuscitation

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Authors: Hadeel Al Obaidy, Amani Al Heela

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The main purpose of this paper is to examine the concept of semantic web and the role that ontology and semantic annotation plays in the development of semantic web services. The paper focuses on semantic web infrastructure illustrating how ontology and annotation work to provide the learning capabilities for building content semantically. To improve productivity and quality of software, the paper applies approaches, notations and techniques offered by software engineering. It proposes a conceptual model to develop semantic web services for the infrastructure of web information retrieval system of digital libraries. The developed system uses ontology and annotation to build a knowledge based system to define and link the meaning of a web content to retrieve information for users’ queries. The results are more relevant through keywords and ontology rule expansion that will be more accurate to satisfy the requested information. The level of results accuracy would be enhanced since the query semantically analyzed work with the conceptual architecture of the proposed system.

Keywords: semantic web services, software engineering, semantic library, knowledge representation, ontology

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Authors: Risanda Alirastra Budiantoro, Riesanda Najmi Sasmita, Sri Herianingrum

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Allah has given the guidance in the form of Islam for Muslim to take and lead all the aspects of life including the economic activity. The Islamic economic system is believed to be the answer to the economic problems that exist at this time. The goal is to achieve falah in kaffah by not doing some economic activities that are in violation as prescribed by Islam. An example for this is riba. Discourse on riba can be said ‘classical’ both in the development of Islamic thought and in Islamic civilization because riba often occurs in all aspects of public life, especially economic transactions (in Islam called muamalah). Riba is an additional retrieval, either in a sale and purchase transaction or lending in a false or contrary to the principle of muamalah in Islam. Prohibition of riba is obtained from various sources by the Qur'an and Hadith Rasulullah SAW, so the scholars firmly and clearly defined the prohibition of riba because there are exploitative elements that can harm the others. So, this study is aimed to identify Islamic economic system and the prohibition of riba in historical perspective. From the results of this study, it is expected to be a good reference for the reader to understand the Islamic economic system and riba in the future.

Keywords: economics system, riba, historical perspective, economy

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Authors: Luvisa Kusuma, Panji Yudha Prakasa

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Cloud storage, as a subservice of infrastructure as a service (IaaS) in Cloud Computing, is the model of nerworked storage where data can be stored in server. In this paper, we propose a secure cloud storage system consisting of two main components; client as a user who uses the cloud storage service and server who provides the cloud storage service. In this system, we propose the protocol schemes to guarantee against security attacks in the data transmission. The protocols are login protocol, upload data protocol, download protocol, and push data protocol, which implement hybrid cryptographic mechanism based on data encryption before it is sent to the cloud, so cloud storage provider does not know the user's data and cannot analysis user’s data, because there is no correspondence between data and user.

Keywords: cloud storage, security, cryptographic protocol, artificial intelligence

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Authors: Meshari Aljohani, Stephan Olariu, Ravi Mukkamala

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Data is essential for enhancing the quality of life. Its value creates chances for users to profit from data sales and purchases. Users in data marketplaces, however, must share and trade data in a secure and trusted environment while maintaining their privacy. The first main contribution of this paper is to identify enabling technologies and challenges facing the development of decentralized data marketplaces. The second main contribution is to propose a decentralized data marketplace framework based on blockchain technology. The proposed framework enables sellers and buyers to transact with more confidence. Using a security deposit, the system implements a unique approach for enforcing honesty in data exchange among anonymous individuals. Before the transaction is considered complete, the system has a time frame. As a result, users can submit disputes to the arbitrators which will review them and respond with their decision. Use cases are presented to demonstrate how these technologies help data marketplaces handle issues and challenges.

Keywords: blockchain, data, data marketplace, smart contract, reputation system

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Authors: Mária Csernoch, Piroska Biró

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The legends about “user-friendly” and “easy-to-use” birotical tools (computer-related office tools) have been spreading and misleading end-users. This approach has led us to the extremely high number of incorrect documents, causing serious financial losses in the creating, modifying, and retrieving processes. Our research proved that there are at least two sources of this underachievement: (1) The lack of the definition of the correctly edited, formatted documents. Consequently, end-users do not know whether their methods and results are correct or not. They are not aware of their ignorance. They are so ignorant that their ignorance does not allow them to realize their lack of knowledge. (2) The end-users’ problem-solving methods. We have found that in non-traditional programming environments end-users apply, almost exclusively, surface approach metacognitive methods to carry out their computer related activities, which are proved less effective than deep approach methods. Based on these findings we have developed deep approach methods which are based on and adapted from traditional programming languages. In this study, we focus on the most popular type of birotical documents, the text-based documents. We have provided the definition of the correctly edited text, and based on this definition, adapted the debugging method known in programming. According to the method, before the realization of text editing, a thorough debugging of already existing texts and the categorization of errors are carried out. With this method in advance to real text editing users learn the requirements of text-based documents and also of the correctly formatted text. The method has been proved much more effective than the previously applied surface approach methods. The advantages of the method are that the real text handling requires much less human and computer sources than clicking aimlessly in the GUI (Graphical User Interface), and the data retrieval is much more effective than from error-prone documents.

Keywords: deep approach metacognitive methods, error-prone birotical documents, financial losses, human and computer resources

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

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This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

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Authors: Mohamed Elkholy, Ahmed Elfatatry

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Cloud computing has emerged as a flexible computing paradigm that reshaped the Information Technology map. However, cloud computing brought about a number of security challenges as a result of the physical distribution of computational resources and the limited control that users have over the physical storage. This situation raises many security challenges for data integrity and confidentiality as well as authentication and access control. This work proposes a security mechanism for data integrity that allows a data owner to be aware of any modification that takes place to his data. The data integrity mechanism is integrated with an extended Kerberos authentication that ensures authorized access control. The proposed mechanism protects data confidentiality even if data are stored on an untrusted storage. The proposed mechanism has been evaluated against different types of attacks and proved its efficiency to protect cloud data storage from different malicious attacks.

Keywords: access control, data integrity, data confidentiality, Kerberos authentication, cloud security

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Authors: Bourama Mane, Ibrahima Fall, Mamadou Samba Camara, Alassane Bah

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At the level of the National Statistical Institutes, there is a large volume of data which is generally in a format which conditions the method of publication of the information they contain. Each household or business data collection project includes a dissemination platform for its implementation. Thus, these dissemination methods previously used, do not promote rapid access to information and especially does not offer the option of being able to link data for in-depth processing. In this paper, we present an approach to modeling these data to publish them in a format intended for the Semantic Web. Our objective is to be able to publish all this data in a single platform and offer the option to link with other external data sources. An application of the approach will be made on data from major national surveys such as the one on employment, poverty, child labor and the general census of the population of Senegal.

Keywords: Semantic Web, linked open data, database, statistic

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Authors: Nazura Abdul Manap, Siti Nur Farah Atiqah Salleh

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For decades, the misuse of personal data has been a critical issue. Malaysia has accepted responsibility by implementing the Malaysian Personal Data Protection Act 2010 to secure personal data (PDPA 2010). After more than a decade, this legislation is set to be revised by the current PDPA 2023 Amendment Bill to align with the world's key personal data protection regulations, such as the European Union General Data Protection Regulations (GDPR). Among the other suggested adjustments is the Data User's appointment of a Data Protection Officer (DPO) to ensure the commercial entity's compliance with the PDPA 2010 criteria. The change is expected to be enacted in parliament fairly soon; nevertheless, based on the experience of the Personal Data Protection Department (PDPD) in implementing the Act, it is projected that there will be a slew of additional concerns associated with the DPO mandate. Consequently, the goal of this article is to highlight the issues that the DPO will encounter and how the Personal Data Protection Department should respond to this subject. The study result was produced using a qualitative technique based on an examination of the current literature. This research reveals that there are probable obstacles experienced by the DPO, and thus, there should be a definite, clear guideline in place to aid DPO in executing their tasks. It is argued that appointing a DPO is a wise measure in ensuring that the legal data security requirements are met.

Keywords: guideline, law, data protection officer, personal data

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Authors: Malki Fatima Zahra Nadia, Kellal Cheiimaa, Brahimi Houria

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Total Quality Management (TQM) is one of the most important modern management systems in marketing, that help organizations to survive and remain competitive in the dynamic market with frequent changes. It assists them in gaining a competitive advantage, growth, and excellence compared to their competitors. To understand the impact of TQM on competitive advantage in economic companies, a study was conducted in Ooredoo Telecommunications Company. A questionnaire was designed and distributed to OOredoo' 75 employees in each of the departments of leadership, quality assurance, quality control, research and development, production, customer service, Similarly, resulting in the retrieval of 72 questionnaires. To analyze the descriptive results of the study, the SPSS software version 25 was used. Additionally, Structural Equation Modeling (SEM) with the help of Smart Pls4 software was utilized to test the study's hypotheses. The study concluded that there is an impact between total quality management and competitive advantage in Ooredoo company to different degrees. On this basis, the study recommended the need to implement the total quality management system at the level of all organizations and in various fields.

Keywords: total quality management, ISO system, competitive advantage, competitive strategies

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Authors: D. Pugazhenthi, B. Sree Vidya

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Cloud computing is one of the emerging technologies that enables end users to use the services of cloud on ‘pay per usage’ strategy. This technology grows in a fast pace and so is its security threat. One among the various services provided by cloud is storage. In this service, security plays a vital factor for both authenticating legitimate users and protection of information. This paper brings in efficient ways of authenticating users as well as securing information on the cloud. Initial phase proposed in this paper deals with an authentication technique using multi-factor and multi-dimensional authentication system with multi-level security. Unique identification and slow intrusive formulates an advanced reliability on user-behaviour based biometrics than conventional means of password authentication. By biometric systems, the accounts are accessed only by a legitimate user and not by a nonentity. The biometric templates employed here do not include single trait but multiple, viz., iris and finger prints. The coordinating stage of the authentication system functions on Ensemble Support Vector Machine (SVM) and optimization by assembling weights of base SVMs for SVM ensemble after individual SVM of ensemble is trained by the Artificial Fish Swarm Algorithm (AFSA). Thus it helps in generating a user-specific secure cryptographic key of the multimodal biometric template by fusion process. Data security problem is averted and enhanced security architecture is proposed using encryption and decryption system with double key cryptography based on Fuzzy Neural Network (FNN) for data storing and retrieval in cloud computing . The proposing scheme aims to protect the records from hackers by arresting the breaking of cipher text to original text. This improves the authentication performance that the proposed double cryptographic key scheme is capable of providing better user authentication and better security which distinguish between the genuine and fake users. Thus, there are three important modules in this proposed work such as 1) Feature extraction, 2) Multimodal biometric template generation and 3) Cryptographic key generation. The extraction of the feature and texture properties from the respective fingerprint and iris images has been done initially. Finally, with the help of fuzzy neural network and symmetric cryptography algorithm, the technique of double key encryption technique has been developed. As the proposed approach is based on neural networks, it has the advantage of not being decrypted by the hacker even though the data were hacked already. The results prove that authentication process is optimal and stored information is secured.

Keywords: artificial fish swarm algorithm (AFSA), biometric authentication, decryption, encryption, fingerprint, fusion, fuzzy neural network (FNN), iris, multi-modal, support vector machine classification

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Authors: Nouha Mhimdi, Wahiba Ben Abdessalem Karaa, Henda Ben Ghezala

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The methods identified in data collection are diverse: electronic media, focus group interviews and short-answer questionnaires [1]. The collection of poor-quality data resulting, for example, from poorly designed questionnaires, the absence of good translators or interpreters, and the incorrect recording of data allow conclusions to be drawn that are not supported by the data or to focus only on the average effect of the program or policy. There are several solutions to avoid or minimize the most frequent errors, including obtaining expert advice on the design or adaptation of data collection instruments; or use technologies allowing better "anonymity" in the responses [2]. In this context, we opted to collect good quality data by doing a sizeable questionnaire-based survey on hospital emergencies to improve emergency services and alleviate the problems encountered. At the level of this paper, we will present our study, and we will detail the steps followed to achieve the collection of relevant, consistent and practical data.

Keywords: data collection, survey, questionnaire, database, data analysis, hospital emergencies

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

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Authors: Daniel Brian Thompson, Subarmaniam Kannan

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The huge weightage of knowledge in this world and within the repository of organizations has already reached immense capacity and is constantly increasing as time goes by. To accommodate these constraints, Big Data implementation and algorithms are utilized to obtain new or enhanced knowledge for decision-making. With the transition from data to knowledge provides the transformational changes which will provide tangible benefits to the individual implementing these practices. Today, various organization would derive knowledge from observations and intuitions where this information or data will be translated into best practices for knowledge acquisition, generation and sharing. Through the widespread usage of Big Data, the main intention is to provide information that has been cleaned and analyzed to nurture tangible insights for an organization to apply to their knowledge-creation practices based on facts and figures. The translation of data into knowledge will generate value for an organization to make decisive decisions to proceed with the transition of best practices. Without a strong foundation of knowledge and Big Data, businesses are not able to grow and be enhanced within the competitive environment.

Keywords: big data, knowledge management, data driven, knowledge creation

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Authors: Masese Chuma Benard, Martin Onsiro Ronald

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Businesses have been using cloud computing more frequently recently because they wish to take advantage of its advantages. However, employing cloud computing also introduces new security concerns, particularly with regard to data security, potential risks and weaknesses that could be exploited by attackers, and various tactics and strategies that could be used to lessen these risks. This study examines data security issues on cloud computing amongst sme’s in Nairobi county, Kenya. The study used the sample size of 48, the research approach was mixed methods, The findings show that data owner has no control over the cloud merchant's data management procedures, there is no way to ensure that data is handled legally. This implies that you will lose control over the data stored in the cloud. Data and information stored in the cloud may face a range of availability issues due to internet outages; this can represent a significant risk to data kept in shared clouds. Integrity, availability, and secrecy are all mentioned.

Keywords: data security, cloud computing, information, information security, small and medium-sized firms (SMEs)

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Authors: M. Strémy, P. Závacký, P. Cuninka, M. Juhás

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Main goal of this paper is to introduce our design of private cloud for storing large amount of data, especially pictures, and to provide good technological backend for data analysis based on parallel processing and business intelligence. We have tested hypervisors, cloud management tools, storage for storing all data and Hadoop to provide data analysis on unstructured data. Providing high availability, virtual network management, logical separation of projects and also rapid deployment of physical servers to our environment was also needed.

Keywords: cloud, glusterfs, hadoop, juju, kvm, maas, openstack, virtualization

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Melaku Tsehay

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The Consortium of Christian Relief and Development Associations (CCRDA), and the CORE Group Polio Partners (CGPP) Secretariat have been working with Global Alliance for Vac-cines and Immunization (GAVI) to improve the immunization data quality in Afar and Somali Regional States. The main aim of this study was to compare the quality of immunization data before and after the above interventions in health facilities in the pastoralist communities in Ethiopia. To this end, a comparative-cross-sectional study was conducted on 51 health facilities. The baseline data was collected in May 2019, while the end line data in August 2021. The WHO data quality self-assessment tool (DQS) was used to collect data. A significant improvment was seen in the accuracy of the pentavalent vaccine (PT)1 (p = 0.012) data at the health posts (HP), while PT3 (p = 0.010), and Measles (p = 0.020) at the health centers (HC). Besides, a highly sig-nificant improvment was observed in the accuracy of tetanus toxoid (TT)2 data at HP (p < 0.001). The level of over- or under-reporting was found to be < 8%, at the HP, and < 10% at the HC for PT3. The data completeness was also increased from 72.09% to 88.89% at the HC. Nearly 74% of the health facilities timely reported their respective immunization data, which is much better than the baseline (7.1%) (p < 0.001). These findings may provide some hints for the policies and pro-grams targetting on improving immunization data qaulity in the pastoralist communities.

Keywords: data quality, immunization, verification factor, pastoralist region

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Authors: Iqbaldeep Kaur, Amarjeet Kaur

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Software Reusability is important part of software development. So component based software development in case of software testing has gained a lot of practical importance in the field of software engineering from academic researcher and also from software development industry perspective. Finding test cases for efficient reuse of test cases is one of the important problems aimed by researcher. Clustering reduce the search space, reuse test cases by grouping similar entities according to requirements ensuring reduced time complexity as it reduce the search time for retrieval the test cases. In this research paper we proposed approach for re-usability of test cases by unsupervised approach. In unsupervised learning we proposed k-mean and Support Vector Machine. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

Keywords: software testing, reusability, clustering, k-mean, SVM

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Authors: Amal Alshahrani, Allan Ramsay

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Paraphrasing is one of the important tasks in natural language processing; i.e. alternative ways to express the same concept by using different words or phrases. Paraphrases can be used in many natural language applications, such as Information Retrieval, Machine Translation, Question Answering, Text Summarization, or Information Extraction. To obtain pairs of sentences that are paraphrases we create a system that automatically extracts paraphrases from a corpus, which is built from different sources of news article since these are likely to contain paraphrases when they report the same event on the same day. There are existing simple standard approaches (e.g. TF-IDF vector space, cosine similarity) and alignment technique (e.g. Dynamic Time Warping (DTW)) for extracting paraphrase which have been applied to the English. However, the performance of these approaches could be affected when they are applied to another language, for instance Arabic language, due to the presence of phenomena which are not present in English, such as Free Word Order, Zero copula, and Pro-dropping. These phenomena will affect the performance of these algorithms. Thus, if we can analysis how the existing algorithms for English fail for Arabic then we can find a solution for Arabic. The results are promising.

Keywords: natural language processing, TF-IDF, cosine similarity, dynamic time warping (DTW)

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

Procedia PDF Downloads 222

Authors: Sanaa Chafik, Imane Daoudi, Mounim A. El Yacoubi, Hamid El Ouardi

Abstract:

Locality Sensitive Hashing (LSH) is one of the most promising techniques for solving nearest neighbour search problem in high dimensional space. Euclidean LSH is the most popular variation of LSH that has been successfully applied in many multimedia applications. However, the Euclidean LSH presents limitations that affect structure and query performances. The main limitation of the Euclidean LSH is the large memory consumption. In order to achieve a good accuracy, a large number of hash tables is required. In this paper, we propose a new hashing algorithm to overcome the storage space problem and improve query time, while keeping a good accuracy as similar to that achieved by the original Euclidean LSH. The Experimental results on a real large-scale dataset show that the proposed approach achieves good performances and consumes less memory than the Euclidean LSH.

Keywords: approximate nearest neighbor search, content based image retrieval (CBIR), curse of dimensionality, locality sensitive hashing, multidimensional indexing, scalability

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

Abstract:

In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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