Big Data Infrastructure, Data Visualisation and Challenges

Venkatraman, Ramanathan; Venkatraman, Sitalakshmi

doi:10.1145/3361758.3361768

Cited by 13 publications

(13 citation statements)

References 18 publications

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“…Desain yang di terapkan tanpa memahami beberapa aspek penting seperti kebutuhan teknologi, kompleksitas teknologi, masalah ketersediaan data, biaya yang tinggi, privasi, dan integritas teknologi. Menjadi solusi yang kurang tepat dalam proses analisis big data sebagai komponen penting dalam pengambilan keputusan [4].…”

Section: Pendahuluanunclassified

“…Kebutuhan akan infrastruktur analitik terintegrasi untuk membantu manajer dalam meningkatkan kinerja operasi dan membongkar silo informasi yang dihasilkan dari analitik data besar [3]. Menggunakan infrastruktur Big Data tanpa memahami masalah biaya tinggi, kompleksitas teknologi, ketersediaan data, privasi, dan integritas mungkin belum tentu menjadi cara yang tepat untuk organisasi mana pun karena Big Data adalah komponen penting dari pengambilan keputusan manajemen yang membutuhkan kapabilitas baru, serta perubahan organisasi dan budaya [4]. Infrastruktur big data yang handal mampu beradaptasi dengan kondisi yang ada yang memberikan kinerja dan kemampuan yang memadai untuk melakukan penambangan informasi untuk membantu dalam pengambilan keputusan [7].…”

Section: Tinjauan Literaturunclassified

“…Tidak banyak makalah akademis yang terkait dengan Big Data, kebanyakan kasus difokuskan pada beberapa teknologi komponen (misalnya Analisis Data atau Pembelajaran Mesin) atau solusi itu mencerminkan hanya sebagian kecil dari keseluruhan area masalah. Sama berkaitan dengan definisi Big Data yang akan memberikan konseptual dasar untuk pengembangan teknologi [4]. Terdapat tantangantantangan yang akan dihadapi peneliti saat memproses big data di setiap tahapan pemrosesan data.…”

Section: Tinjauan Literaturunclassified

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Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis

2022

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Infrastruktur Big Data merupakan teknologi yang menyediakan kemampuan untuk menyimpan, memproses, menganalisis, dan memvisualisasikan data yang berukuran besar. Alat dan aplikasi yang digunakan menjadi salah satu tantangan ketika membangun infrastruktur big data. Pada penelitian ini kami menawarkan sebuah strategi baru guna mengoptimasi desain infrastruktur big data yang merupakan bagian penting dalam pengolahan big data dengan cara melakukan analisis kinerja aplikasi yang digunakan pada setiap tahap pemrosesan big data. Proses diawali dari mengumpulkan data yang bersumber dari berita online dengan menggunakan metode web crawler dengan menerapkan aplikasi Scrapy dan Apache Nutch. Selanjutnya menerapkan teknologi Hadoop untuk mempermudah dalam penyimpanan dan komputasi big data secara terdistribusi. No-SQL database Mongo DB dan HBase untuk mempermudah melakukan query data, Setelah itu membangun mesin pencari menggunakan Elasticsearch dan Apache Solr. Data yang tersimpan kemudian di analisis menggunakan Apache Hive, Pig dan Spark. Data yang telah dianalisis kemudian ditampilkan dalam website menggunakan aplikasi Zeppelin, Metabase, Kibana dan Tableau. Skenario pengujian terdiri dari jumlah server dan file yang digunakan. Parameter pengujian mulai dari kecepatan proses, penggunaan memori, penggunaan CPU, throughput dll. Hasil pengujian kinerja setiap aplikasi kemudian dibandingkan dan dianalisis untuk melihat kelebihan dan kekurangan aplikasi sebagai referensi dalam membangun desain infrastruktur yang optimal sesuai dengan kebutuhan penggunanya. Penelitian ini telah menghasilkan dua alternatif desain infrastruktur big data. Infrastruktur yang disarankan sudah diimplementasikan pada node-node komputer di Lab Big Data Pens untuk mengolah big data dari media online dan terbukti dapat berjalan dengan baik.

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Section: Pendahuluanunclassified

Section: Tinjauan Literaturunclassified

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Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis

2022

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“…We have split the output data in two parts: 1. Tabular component [20] -we get some numerical data, which represent new knowledge about the array of urban real estate, obtained from the full set of data for the entire period; 2. Visual component -allows you not only to get numbers, but also to see them in a visual form.…”

Section: Output Data and Its Presentationmentioning

confidence: 99%

Big data analysis for studying spatiotemporal trends in the sustainable development of large cities

Mulyukova

Kurejchik

2021

E3S Web Conf.

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The article covers the analysis of big data in urban planning. The purpose of this work is to study modern problems of processing big data containing information about real estate objects and prospects for solving these problems, as well as the possibility of practical implementation of the methodology for processing such data sets by designing and filling a special graphic abstraction “metahouse” using a practical example. The relevance of the study lies in identifying a number of advantages in the presentation of data in graphical form. The mathematical basis of the technique is the use of multidimensional spaces, where measurements are the characteristics of individual objects. In the course of the work, the specifics of big data sets, consisting of information about real estate in a large city, were described. methods of effective solution of the set practical problem of processing and searching for patterns in a large data array were proposed: abstraction “metahouse”, data aggregator. In the course of the study, it was revealed that the presentation of groups of the obtained data in a graphical form has a number of advantages over the tabular presentation of data. The obtained results can be used both for the primary study of big data processing technologies, and as a basis for the development of real applications in the following areas: analysis of changes in the area of houses over time, analysis of changes in the number of storeys in urban development, etc.

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“…One of the most difficult tasks associated with big data is that of organising, analysing, and presenting data to users in a manner that supports sensemaking (Venkatraman and Venkatraman, 2019 ). Visualisations are a means by which sensemaking can be achieved more rapidly than through text (Ware, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

Requirements for a Dashboard to Support Quality Improvement Teams in Pain Management

et al. 2021

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Pain management is often considered lower priority than many other aspects of health management in hospitals. However, there is potential for Quality Improvement (QI) teams to improve pain management by visualising and exploring pain data sets. Although dashboards are already used by QI teams in hospitals, there is limited evidence of teams accessing visualisations to support their decision making. This study aims to identify the needs of the QI team in a UK Critical Care Unit (CCU) and develop dashboards that visualise longitudinal data on the efficacy of patient pain management to assist the team in making informed decisions to improve pain management within the CCU. This research is based on an analysis of transcripts of interviews with healthcare professionals with a variety of roles in the CCU and their evaluation of probes. We identified two key uses of pain data: direct patient care (focusing on individual patient data) and QI (aggregating data across the CCU and over time); in this paper, we focus on the QI role. We have identified how CCU staff currently interpret information and determine what supplementary information can better inform their decision making and support sensemaking. From these, a set of data visualisations has been proposed, for integration with the hospital electronic health record. These visualisations are being iteratively refined in collaboration with CCU staff and technical staff responsible for maintaining the electronic health record. The paper presents user requirements for QI in pain management and a set of visualisations, including the design rationale behind the various methods proposed for visualising and exploring pain data using dashboards.

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Big Data Infrastructure, Data Visualisation and Challenges

Cited by 13 publications

References 18 publications

Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis

Big Data Infrastructure Design Optimizes Using Hadoop Technologies Based on Application Performance Analysis

Big data analysis for studying spatiotemporal trends in the sustainable development of large cities

Requirements for a Dashboard to Support Quality Improvement Teams in Pain Management

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