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IoT & Big Data Based Applications for Restaurant Questions of Opportunities, Challenges, Benefits & Operations

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Technical Report

IoT & Big Data Based Applications for Restaurant Questions of Opportunities, Challenges, Benefits & Operations

Abstract

Internet of Things (IoT) & Big Data are considered as one of the big benefits for business. So, many types of organisations use IoT and big data technology for various purposes not limited to business automation, business intelligence and marketing. IoT and big data can play a great role in restaurant industry. But these technologies are not widely used in the restaurant industry. This paper proposes an IoT and big data based system customized for restaurants and evaluates its opportunities, benefits and challenges. It also explains how the system can be integrated to the operations of restaurant.
IoT & Big Data Based Applications for Restaurant
Questions of Opportunities, Challenges, Benefits & Operations
Md. Muminur Rahman
School of Computing & Mathematics
University of Derby
m.rahman23@unimail.derby.ac.uk
Abstract. Internet of Things (IoT) & Big Data are considered as one of the big
benefits for business. So, many types of organisations use IoT and big data tech-
nology for various purposes not limited to business automation, business intelli-
gence and marketing. IoT and big data can play a great role in restaurant industry.
But these technologies are not widely used in the restaurant industry. This paper
proposes an IoT and big data based system customized for restaurants and eval-
uates its opportunities, benefits and challenges. It also explains how the system
can be integrated to the operations of restaurant.
Keywords: IoT, Internet of Things, Big Data, Restaurant, Hadoop, HDFS, Big
Data Analytics
Table of Contents
1 Introduction ..................................................................................................... 3
1.1 Definition of Internet of Things & Big Data ........................................... 3
2 Research Methodology .................................................................................... 3
3 Customized Solution for Restaurant ................................................................ 3
4 Operations........................................................................................................ 4
4.1 Data Input ............................................................................................... 4
4.2 Data Processing ....................................................................................... 4
4.3 Result ...................................................................................................... 5
5 Opportunities ................................................................................................... 5
5.1 Ubiquitous Connectivity ......................................................................... 5
5.2 Miniaturisation ........................................................................................ 5
5.3 Rise of Cloud Computing ....................................................................... 5
6 Benefits ............................................................................................................ 5
6.1 Kitchen Monitoring ................................................................................. 6
6.2 Reducing Food Waste ............................................................................. 6
6.3 Restaurant Labour Management ............................................................. 6
7 Challenges ....................................................................................................... 6
7.1 Naming and Identity Management .......................................................... 6
7.2 Information Privacy ................................................................................ 6
7.3 System Protection ................................................................................... 7
7.4 Data Quality ............................................................................................ 7
8 Conclusion & Recommendation ...................................................................... 7
9 Bibliography ................................................... Error! Bookmark not defined.
1 Introduction
IoT and big data technology are used in many different aspects of life and business,
not limited to vehicles, factory, and smart home. Almost 8.4 billion of connected ob-
jects will be in use 2017 and 20.4 billion device are connected to the internet by 2020
(Gartner, 2017b). Cisco forecasts more than 24 billion Internetconnected objects by
2019. The variability and differences in these predictions make any specific number of
IoT device questionable. But these sources illustrate the significant growth of IoT and
big data (Khan et al., 2012). The restaurant industry can leverage the IoT and big data
like others. But the use of these technologies is fewer in restaurant industries than other
industries. So, this paper shows how restaurants can harness the power of IoT and big
data to make daily operations more productive and what challenges they may face in
order to introduce these technologies. The paper also proposes a theoretical IoT and big
data based solution that could be used in restaurants.
1.1 Definition of Internet of Things & Big Data
British technology pioneer Kevin Ashton coined the term Internet of Things, IoT in
short form, in 1999. He used the term to describe a system in which the objects from
real world can be connected through internet using sensors. (Rose, Eldridge and Cha-
pin, 2015) There is not any universal definition for the Internet of Things. After com-
paring different definitions, Arabsorkhi et al. (2016) define IoT as a collection of ob-
jects that are connected to each other via the Internet and with ability of communicating
and sharing information among each other in a smart manner”.
There are also different definitions for big data. But all of them uses some common
attributes. Gartner defines big data as “high-volume, high-velocity and/or high-variety
information assets that demand cost-effective, innovative forms of information pro-
cessing that enable enhanced insight, decision making, and process automation(Gart-
ner, 2017a).
2 Research Methodology
A simple research methodology has been used in this paper. The main resource of
this paper is various journal articles. A wide range of journal articles, webpages, online
articles and white papers have been reviewed to extract the opportunities, benefits and
challenges of IoT and big data based application in restaurant industry.
3 Customized Solution for Restaurant
Saeed et al. (2016) implemented an IoT based application for restaurant, which helps
customers finding free parking lot and free table, ordering foods and paying bills from
their mobile phone. On the other hand, the management can monitor the whole work
properly. But they did not use the power of big data which can bring more attractive
functions. This analysis proposes a conceptual model of IoT and big data based appli-
cation for restaurant.
For customers, there will be a mobile application which allows them to order food
online or in restaurant. Customer’s all types of purchase data will be saved in the data-
base. Data can be collected from different sources. Such as, the restaurant app in cus-
tomer’s phone has the information of the customer. The app also sends promotional
offers as notification so that customers get information about new offers.
The customer’s location can be tracked with the restaurant app which will allow
managers to do geo-marketing. The social media can be integrated to the main ERP
system which allows customer to find information and service faster. It also allows the
management to get a real-time sentiment insights. So, they can make decisions and
create useful promotional offer (Shankararaman and Lum, 2013). Kitchen IoT sensors
will be connected to the system, so managers get the real-time information about what
is going on in the kitchen. When customers order food from the table using their mobile
application, the order list will be shown to the kitchen in real-time. It will reduce the
time of placing order. On the other hand, customer can know that approximately how
much he or she has to wait for the ordered item (Saeed et al., 2016).
4 Operations
The operations of the proposed IoT and big data system in restaurant are divided into
three phases. The three phases are explained below.
4.1 Data Input
The main fuel of the system is data. The sources of data are customers’ mobile ap-
plication, location, health information, restaurant ERP, relational databases, POSs,
smart objects, sensors and social data. The data is collected from variety of sources and
types which is processed in real-time (National Restaurant Association (NRA), no
date).
4.2 Data Processing
The volume, variety and velocity of collected data of restaurant leads to using a big
data technology. Hadoop ecosystem can be used for storing and processing data. One
of the main component of Hadoop ecosystem Hadoop Distributed File Systems (HDFS)
will be used for storing these large amount of data. HDFS uses commodity servers to
store files in distributed system. MapReduce will be used for the processing of the data
with HDFS (Silvy, 2014).
4.3 Result
After processing the data, there will be an output. For getting the request from users
(eg. Managers, customers), Yarn will be used which is a REST API web service and
used under Hadoop ecosystem. Yarn will get request from users and send the request
to the system. MapReduce and HDFS work based on the request and send the result
which will Yarn show to the user (Jones and Nelson, 2013).
5 Opportunities
The opportunities of the proposed IoT and big data based application for restaurants
are discussed below.
5.1 Ubiquitous Connectivity
Internet connection can be found anywhere. Many restaurants now provide with free
Wi-Fi service which makes the use of mobile phones popular. It also a big opportunity
for this IoT and big data based application as it can get more information about cus-
tomers from the internet. The wide connectivity also lets IoT devices to contact through
internet with ease and properly (Rose, Eldridge and Chapin, 2015).
5.2 Miniaturisation
Gadgets and electrical sensors are getting smaller but more powerful which encour-
ages people to get smart objects. Using cell phone is being a common trend which also
motivates the use of IoT for restaurant (Khan et al., 2012). With the wide use of cell
phones, getting insights of customers and sending product information are being easier
(National Restaurant Association (NRA), no date).
5.3 Rise of Cloud Computing
Cloud technology provides an easy way to handle IoT applications and big data gen-
erated from IoT. It is easy set up an IoT based system with cloud technology. Because
most of popular cloud service providers (e.g. Amazon Web Services (AWS) and Mi-
crosoft Azure) provide different easy-to-install cloud services for IoT and big data,
which does not need specific high configuration computers (Rose, Eldridge and Chapin,
2015).
6 Benefits
IoT and big data based applications can leverage some benefits for restaurants. Some
of the major and common benefits are given below.
6.1 Kitchen Monitoring
IoT based applications are used for smart kitchen. It enables manager to get a clear
insight of the foods and its ingredients. For example, IoT based application can help in
monitoring the quality of oil. It can measure the amount of oil being used in food. If the
quality of oil is poor or the amount of oil in a food is excessive it will make a notifica-
tion. It can also monitor the food quality in refrigerators (Mogali, 2015; Kiesel, 2017).
6.2 Reducing Food Waste
Verrill (2016) states that each year worldwide about 1.3 billion tons of food are
wasted and around 40% of that is by restaurants. Big data analytics can be used for
analysing past history, weather data, location data and point of sales (POS) data to get
an insight of customer volume. Food will be made based on that insights which will
lead to less food wastage (Abudheen K, no date).
6.3 Restaurant Labour Management
It's easy to schedule too many or too few employees, especially if the management
is unsure when restaurant’s busiest hours are. Big data analytics can be used in this case
to track sales pattern and analyse past data. Based on the insight of the sales pattern,
they can plan for labour management which will cut extra labour cost (Smith, 2015).
7 Challenges
There are some challenges that IoT & big data based applications may face. Those
are following.
7.1 Naming & Identity Management
Many sensors are connecting to internet. So, there should be a stable naming and
identity management system which can dynamically assign and manage unique identity
of the objects. But there is not any universal standard for that (Khan et al., 2012).
7.2 Information Privacy
IoT devices gather a lot of information which raised the question on the privacy of
the information of the customers. For example, customer’s location has been collected
from devices for geo marketing. So attacker can breach the system and know that when
the customer visits the restaurant. So, information privacy mechanism like perturbation-
based privacy preserving schemes should be used to preserve data security (Lin et al.,
2017). On the other hand, there are different technologies to identify a smart object in
a network, such as 2D barcodes and RFID. These objects carry their identification tags
and object specific information. So, it is essential to take privacy measures and prevent
illegal access. Encryption techniques must be used for the system (Khan et al., 2012).
7.3 System Protection
One of the biggest challenges for any kind of internet based system is hacking. When
smart objects are connected to the internet the chance of being hacked also increases.
A Germen security researcher Jens Regel revealed a risk in the industrial Miele Profes-
sional PG 8528 connected dishwasher. This kind of smart dishwasher are used in dif-
ferent facilities like hospitals and restaurants. There was a bug in the dishwasher which
may allow hackers to infiltrate malware on the dishwasher and use it to bounce off onto
other connected devices. This kind of vulnerabilities can be found on any smart device
which will make the system vulnerable (PYMNTS, 2017). To reduce the vulnerability
of the system, governance standards (e.g. ISO 27000, ISO 31000 & ITIL) should be
implemented for making and managing such system.
7.4 Data Quality
Big data is all about data. If the data is not accurate it may lead to false reasoning
and wrong decision. John Easton of IBM stated that 80% of available big data is uncer-
tain (Chopra and Madan, 2015). Xiang et al. (2017) wrote a paper that examined the
reliability and trustworthiness of social media data by mining TripAdvisor hotel re-
views. They stated a warning about using data obtained from customer reviews on so-
cial media sites for research. Because the quality of data obtained from social media
reviews can mislead (Virginia Tech Business, 2017). Because the reviews given by cus-
tomers are not always laid on the attributes of the restaurant. But the reviews could also
be affected by weather and demographical factors as Bakhshi et al. (2014) proved. Per-
fect algorithms, data cleansing methods and reasoning techniques should be used to
avoid pitfalls come from poor quality of data.
8 Conclusion & Recommendation
This paper proposed an IoT and big data based system for restaurants and described
its benefits, challenges and opportunities. This IoT and big data based system can solve
many problems for restaurants and make the daily operations of restaurant faster, less
time consuming, easier and more efficient with reduced cost. But the restaurants need
trained people to maintain this kind of systems which can cause extra budget. So, cost-
benefit analysis should be conducted and the feasibility should be checked before im-
plementing the system. The proposed system was focused to the Hadoop technology.
But the system can also be made on the cloud system, such as Amazon Web Services.
9 Bibliography
1. Abudheen K, S. (no date) This food startup uses Big Data to predict users’ habits & cut
wastage, e27. Available at: https://e27.co/this-food-startup-uses-big-data-to-predict-users-
habits-cut-wastage-20150603/ (Accessed: 21 April 2017).
2. Arabsorkhi, A., Haghighi, M. S. and Ghorbanloo, R. (2016) ‘A conceptual trust model for
the Internet of Things interactions’, in 2016 8th International Symposium on Telecommuni-
cations (IST). 2016 8th International Symposium on Telecommunications (IST), pp. 8993.
doi: 10.1109/IS℡.2016.7881789.
3. Bakhshi, S., Kanuparthy, P. and Gilbert, E. (2014) ‘Demographics, Weather and Online Re-
views: A Study of Restaurant Recommendations’, in Proceedings of the 23rd International
Conference on World Wide Web. New York, NY, USA: ACM (WWW ’14), pp. 443454.
doi: 10.1145/2566486.2568021.
4. Chopra, A. and Madan, S. (2015) ‘Big Data: A Trouble or A Real Solution?’, International
Journal of Computer Science Issues, 12(2), pp. 221229.
5. Gartner (2017a) Big Data, Gartner. Available at: http://www.gartner.com/it-glossary/big-
data (Accessed: 29 April 2017).
6. Gartner (2017b) Gartner Says 8.4 Billion Connected ‘Things’ Will Be in Use in 2017, Up 31
Percent From 2016 [Press release], Gartner. Available at: http://www.gartner.com/news-
room/id/3598917 (Accessed: 24 April 2017).
7. Jones, M. and Nelson, M. (2013) Moving Ahead with Hadoop YARN, IBM developerWorks.
Available at: http://www.ibm.com/developerworks/library/bd-hadoopyarn/index.html (Ac-
cessed: 8 May 2017).
8. Khan, R., Khan, S. U., Zaheer, R. and Khan, S. (2012) ‘Future Internet: The Internet of
Things Architecture, Possible Applications and Key Challenges’, in 2012 10th International
Conference on Frontiers of Information Technology. 2012 10th International Conference
on Frontiers of Information Technology, pp. 257260. doi: 10.1109/FIT.2012.53.
9. Kiesel, J. (2017) The Internet of Things in Restaurants, Manufacturing Tomorrow. Available
at: http://manufacturingtomorrow.com/article/2017/03/the-internet-of-things-in-restau-
rants/9261 (Accessed: 21 April 2017).
10. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H. and Zhao, W. (2017) ‘A Survey on Internet
of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications’,
IEEE Internet of Things Journal, PP(99), pp. 11. doi: 10.1109/JIOT.2017.2683200.
11. Mogali, S. S. (2015) ‘Internet of Things and its role in Smart Kitchen’, in. 4th National
conferrence of Scientometrics and Internet of Things, India. Available at: https://www.re-
searchgate.net/publication/287878645_Inter-
net_of_Things_and_its_role_in_Smart_Kitchen (Accessed: 21 April 2017).
12. National Restaurant Association (NRA) (no date) ‘Big Data and Restaurants: Something to
Chew On’. National Restaurant Association (NRA). Available at: http://www.restau-
rant.org/BigData (Accessed: 1 May 2017).
13. PYMNTS (2017) Any IoT Device Can Get Hacked, Even Dishwashers, PYMNTS.com.
Available at: http://www.pymnts.com/internet-of-things/2017/any-iot-device-can-get-
hacked-even-dishwashers-cybersecurity/ (Accessed: 27 April 2017).
14. Rose, K., Eldridge, S. and Chapin, L. (2015) ‘The Internet of Things: An Overview Under-
standing the Issues and Challenges of a More Connected World’. Edited by C. Marsan. The
Internet Society (ISOC). Available at: https://www.internetsociety.org/sites/de-
fault/files/ISOC-IoT-Overview-20151221-en.pdf (Accessed: 26 March 2017).
15. Saeed, H., Shouman, A., Elfar, M., Shabka, M., Majumdar, S. and Horng-Lung, C. (2016)
‘Near-field communication sensors and cloud-based smart restaurant management system’,
in 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT). 2016 IEEE 3rd World
Forum on Internet of Things (WF-IoT), pp. 686691. doi: 10.1109/WF-IoT.2016.7845440.
16. Shankararaman, V. and Lum, E. K. (2013) ‘Integration of Social Media Technologies with
ERP:A Prototype Implementation’, AMCIS 2013 PROCEEDINGS, p. 11.
17. Silvy, N. (2014) ‘What is Hadoop and How Does It Work?’, Dataconomy, 2 February.
Available at: http://dataconomy.com/2014/02/hadoop-what-how-introduction/ (Accessed: 8
May 2017).
18. Smith, D. P. (2015) The Big Business of Big Data, QSR magazine. Available at:
https://www.qsrmagazine.com/restaurant-software/big-business-big-data (Accessed: 24
April 2017).
19. Verrill, C. (2016) American restaurants are wasting an incredible amount of food here’s
the proof, Business Insider. Available at: http://www.businessinsider.com/solving-food-
waste-in-americas-restaurants-2016-5 (Accessed: 21 April 2017).
20. Virginia Tech Business (2017) ‘Big Data, Low Reliability’, 30 March. Available at:
http://www.magazine.pamplin.vt.edu/issues/spring-2017/big-data-low-reliability/ (Ac-
cessed: 16 April 2017).
21. Xiang, Z., Du, Q., Ma, Y. and Fan, W. (2017) ‘Assessing Reliability of Social Media Data:
Lessons from Mining TripAdvisor Hotel Reviews’, in Information and Communication
Technologies in Tourism 2017. Springer, Cham, pp. 625638. doi: 10.1007/978-3-319-
51168-9_45.
... Restoranlarda nesnelerin interneti teknolojisinden faydalanılarak sensör donanımlı ekipmanlar sayesinde mutfaktaki ekipmanların bakımı gerçekleştirilir ve daha az insan gücünden faydalanabilinir (Deloitte Digital, 2016). Bu sayede restoranların iş gücü maliyetlerinde önemli ölçüde azalma meydana gelir (Rahman, 2016). Nesnelerin interneti dışında restoran işletmeri büyük veri teknolojisinden de faydalanabilir. ...
... Restoranlarda nesnelerin interneti teknolojisinden faydalanılarak sensör donanımlı ekipmanlar sayesinde mutfaktaki ekipmanların bakımı gerçekleştirilir ve daha az insan gücünden faydalanabilinir (Deloitte Digital, 2016). Bu sayede restoranların iş gücü maliyetlerinde önemli ölçüde azalma meydana gelir (Rahman, 2016). Nesnelerin interneti dışında restoran işletmeri büyük veri teknolojisinden de faydalanabilir. ...
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As an emerging research paradigm, big data analytics has been gaining currency in various fields. However, in existing hospitality and tourism literature there is scarcity of discussions on the quality of data which may impact the validity and generalizability of research findings. This study examines the reliability of online hotel reviews in TripAdvisor by developing a text classifier to predict travel purpose (i.e., business versus leisure) based upon review textual contents. The classifier is tested over a range of cities and data sizes to examine its sensitivity to data samples. The findings show that, while the classifier’s performance is fairly consistent across different sets of cities, there are variations in response to data sizes and sampling methods. More importantly, a considerable amount of noise is found in the data, which leads to misclassification. Furthermore, a novel approach is developed to address the misclassification problem resulting from data noise. This study reveals important data quality issues and contributes to the theoretical foundations of social media analytics in hospitality and tourism.
Technical Report
Full-text available
Scientometrics & Internet of Things (SIOT) : Report of the 4th National conference Organised by Institute of Scientometrics and NCSI-Net Date: 25-26 September 2015. Venue: the Solitaire Hotel, Bangalore 560001 Reported by : Dr.R.B.Gaddagimath , Gulbarga University , Gulbarga Dr.S.L.Sangam Karnatak University,Dharwad 580003 slsangam@gmail.com The 4th National conference of the Institute of Scientometrics on ‘Scientometrics and the Internet of Things (SIOT)’ has started with an invocation. The dais was occupied by inaugural speaker Dr. Abhinanda Sarkar, Prof. V.G. Talawar, president of the occasion, Dr.S.L.Sangam Conference Chair, Prof. T.D. Kemparaju, organizing chair, Dr. I.R.N. Goudar, technical Director and Rapporteur General Dr. R.B. Gaddigimath. The occasion has kick started by lightning the auspicious lamp with traditional customs by the dignitaries. Mr. Anand T Byrappa, organizing secretary welcomed the gathering and highlighted the theme of the conference. Prof. S. L. Sangam, the conference chair introduced the Institute of Scientometrics, its origin, establishment, development, activities and also briefed about the developments in the area of Scientometrics and the making of the 4th conference at the national level on the theme “Scientometrics and the Internet of Things (IoT)”.There were 200 deligates and 59 papers presented. The inaugural speaker Dr. Abhinanda Sarkar, Co-founder, Omix Lab in his inaugural address highlighted the developments in Scientometrics and the Internet in evaluating impact and output analysis in his talk entitled “Machines, Metrics and Measures”. In his address he opined that the Internet and ICTs have played a big role in the present day world. He opined that at present for every 60 seconds – there are 98,000 tweets by people; 695,000 status updates takes place; 11 million instant messages exchanged; 698,445 Google Searches conducted worldwide; 168 million emails sent/ received; 1,820 TB of data created; and 271000 users use and download mobile web applications.Dr. Sarkar went on and highlighted the smart mind, smart homes, smart office, smart cities, smart phones as well as smart apps and stressed on the initiative by the prime minister of India Shri. Narendra Modiji’s on smart cities, Make in India and digital India.Dr. Sarkar also mentioned in length about Internet of Things (IoT); Connectivity of Objects & talking each other through internet; networking; Machines controlled by machines using sensors; Application of IoT in libraries - how best the concept of IoT can be integrated in the modern day libraries and Information centers; automatic controlling lights in reading areas; automatic controlling AC/Heater in humid conditions and to automatically check the availability of computers/printers whenever needed.Dr. Abhinanda Sarkar concluded his inaugural speech that he is looking forward to a brave new world of rapid dynamic things and activities enabled by the Internet of Things. The keynote address was delivered by Dr. Guido Zosimo-Landlfo (Germany) the publishing editor of Scientometrics and international journal of repute. In his keynote address he said that the internet was created by things. The problems encountered by him as an editor of Scientometrics Journal were presented. The problems of plagiarism; coping with copying, Fake Research Papers, Fake peer reviewing, self-citations and other publishing related areas. Book on “Scientometrics” written by Prof. S. L. Sangam, the conference chair was released by Dr. V. G. Talawar. In his presidential address Dr. Talawar hoped that the universities in the country covered prescribed the book on “Scientometrics” by Dr. S. L. Sangam as text book for PG (LIS) studies. Summarizing the keynotes presentation by Guido and chief guests lead presentations expressed that the deliberations during the conference would be quite educating, interesting and opportunities for knowledge gain.Prof. T.D. Kemparaju rendered the vote of thanks. Prior to the conference there were two tutorials which gave practical experience to the deligates: First Day -Tutorial 1: 25th Sept. 2015 9.00 to 10.30 am. ALTMETRICS by Prof. S L Sangam, Dr. Shamprasad Pujar and Dr. Satish Munnoli Altmetrics or ‘alternative-to-metrics’ is a hot buzzword in the scholarly world often raising questions such as: What it is? How it is used? How it is different? How it is computed? It is a new method of calculating the impact of research using social media applications by counting the number of times a particular piece of research mentioned or quoted or discussed. This new method has evolved, as scholarly communication is increasingly moving towards online and which has made available new indicators in the form of bookmarks, blog posts and tweets to assess the impact of research as compared to conventional metrics such as counting citations of an article. To simplify the process of tracking and measuring the impact of research on social media, some of the tools have evolved, notably Altmetric, ImpactStory and PlumAnalytics, which have been increasingly used by individual researchers, institutions and publishers visualizing the current needs. This tutorial has focused on dissecting the concept of ‘altmetrics’ and analyze the computation methods adopted behind altmetric scores. There was a demo on ‘Altmetric Explorer’ tool and a demo on how best this can be used for undertaking alternative metric studies by LIS professionals with suitable examples discussed. Second Day - Tutorial 2: 26th Sept.2015 9.00 to 10.30 am. DATABASES FOR SCIENTOMETRIC RESEARCH TECHNIQUES AND DATA ANALYSIS by Dr. I.R.N. Goudar, Ms. Tahseen Afroz Khandey, Elsevier A number of bibliometric and non-bibliometric indicators are used for quantification of research output of an individual or an institute. The concept of Scientometircs and its applications for quantification of research have given fairly well accepted indices. In this direction, widely used indices are counts of publications, citations and h-index. h-index invented by Hersh, a physicist. h-index is being used by most of the institutions and grant providing bodies for recruiting, promoting researchers and sanctioning research grants. Web of Science and Scopus serve as tools to derive important scientometric indicators like average number of citations per paper. These tools serve for authorship, foreign collaboration and research grants studies. Google Scholar and Microsoft Academic Index also facilitate calculating these indices. While one can easily derive h-index of an individual using Google Scholar, it is difficult to use this tool for research output of an institution. Google scholar also provides an alternative indicator called i-10 index. A number of ready to use to open source tools like Scholarometer facilitate calculation of h-index, which of course uses Google scholar data in the background. Journal Citation Reports provide tool for critically evaluating journals based on citation data. SCImago, an open access portal, gives ranked journals and countries scientific indicators based on Scopus database.”InCites” is a customized, citation-based research analytics tool offered by Thomson Reuters for evaluating institutional productivity. Elsevier’s “SciVal” offers access to the research performance of 5,500 research institutions and 220 nations worldwide. Dr. Goudar made a presentation on the theme which emphasis on research evaluation. Bibliometric indications like Publications; Citations; Citations based on indices (research excellence like h-index, g-index); Collaboration; Journal Input factor (ISI/JCR); The Scimago journal ranking; Altmetrics; Non publications indicators and Patents of credit were dealt in detail. Ms. Tahseen Afroz Khandey from Elsevier highlighted the new features and facilities added and available within Scopus, which helps in measuring scholarly output. Features of scival were highlighted as well. There were four plenary talks delivered by eminent professioanls from LIS community as well as from IoT discipline are as listed below; • Plenary Talk 1: “Modelling/ Optimization” By Prof. Snehanshu Saha, Professor, Computer Science, PES University • Plenary Talk 2: “Scalable IoT” By Francis daCosta (USA), Founder/CTO, MeshDynamics. • Plenary Talk 3: “‘Internet of Things’ as socio-technical dimension to ‘Scientometrics 3.0’” by Dr. H.S. Siddamallaiah, former Principal LIS officer and visiting professor, Mahasarakham University, Thailand. • Plenary Talk 4: “Usage Metrics and Their Applications” by Tahseen Afroz Khanday, Elsevier Science The whole SIoT was divided in 5 broad Technical Sessions: Technical Session 1: Internet of Things The session was chaired by Dr. Franics Jayakanth and Dr. H. Rajendra Babu was the rapporteur. Twelve papers were presented in this session. The session of Internet of Things (IoT) discussed on Scimaso Journal and country ranking; Internalization of Journals; Source Normalized Impact Factor (SNIP); impersonal citation counts; journals international modeling index (JIMI); journals reference score; smart applications on IoT enabled libraries; smart kitchens and smart library management etc. Technical Session 2: Scientometrics: Contents, Evolution and Loss The session was chaired Prof. Prahlad G. Tadsad and Mr. G. Sharanabasava was the rapporteur. Seventeen papers were presented in this session on “Scientometric concepts, evolution and laws”. Interesting presentation made by the authors on applications, mapping S & T Indicators. Zif’s law were very well received Fare book, posts, Lotka’s law and case studies on Nephorolgy research output in India, Bio-fuel literature robotics, academic librarianship, Architecture thesis and Geroge E. Smith presentations were presented. There was lively discussion. Technical Session 3: Scientometrics Applications: Mapping and S & T indicators. Dr. M. S. Sridhar chaired the session and Dr. M. Anjanappa was rapporteur. Eight papers were presented in this session on “Scientometric Applications Mapping and S & T Indicators” use of social media, webology, mapping resource on bamboo research literature; cryogenics and critical analysis of scientific products were dealt. Dr. M.S. Sridhar in his concluding remarks said that the data should be accessible (not proprietary) transparent testifiable, replicable and comprehensive. He said that citation within a document historical influentiality of a journal, peer judgment; online access/use downloads like comments, online rating etc., are not only the metrics and the measures of impact entities (56 measures). Technical Session 4: Scientometrics Applications: Communications & Collaboration The session was chaired by Prof. B. S. Biradar and Mr. G. Govindareddy served as raporteaur. Ten papers were presented in this session.The session was broadly dealt on Scientometric applications, communications and collaborations. Papers on authorship pattern, productivity patterns Scientometric analysis of faculty publications, Webometric analysis of top 10 Asian universities were presented. Technical Session 5: Scientometrics Portraits and Subjects Studies The session was chaired by Dr. S. K. Savanur and Dr. B. Nagappa was the rapporteur.Twelve papers were presented in this session. The session broadly dealt on Scientometric portraits and subject studies. Interesting presentations on priority profile of major countries in the field of genetics, Hydraulic power research, Bibliometric study on Girish Kumar and Dr. Gurudev S. Khush and case studies of veterinary science, chemical science, and cardiac anesthesia were made. There were comments, interaction and discussion on the presented of papers. Panel Discussion was the added attraction of the conference.Theme: Scientometrics of IoT: Synergies Opportunities and Challenges for LIS. Moderator:N.V. Sathyanarayana, CMD, Informatics India Pvt. Ltd., Bangalore .Panelists: 1)Dr. N Paravatamma, Professor, Dept. of Library and Information Science, Gulbarga University.2)Dr. Puneet Vanjan, Executive Officer, IoT Operations, TCS, Bangalore 3)Dr. H.S. Siddamallaiah, former Principal LIS officer (Rtd.) and also visiting professor, Mahasarakham University, Thailand. There were three product presentations, most useful and informative from the point of view of Library and Information centers and professionals, made by the following publishing companies as part of their support in the form of sponsorship as follows: 1. Springer – Platinum Sponsor by Kanak Soni 2. EBSCO – Silver Sponsor by Roby Mathew 3. ACD Labs – by K K Bagh Chandani Best paper Award: In consultation with the chair persons of different sessions have obtained the names and passed on the names to the organizers for finalization best Paper awarded.Prof.Sangam Best Paper awarded to Smt.Gidney et al. citation with Rs.1000/- cash. Fellow Institute of Scientometrics: Dr.Ronald Rousse Ph.D (Mathematics) Ph.D (Library and Information Science) Associate Professor KHBO, Ostend Belgium. President International Society for Scientometrics and Informetrics ( ISSI).Highly cited researcher in 2014.Awarded “Fellow Institute of Scientometrics” for the year 2015 for his lifelong achievement in the field of Scientometrics. He will be given Rs.10, 000/- Cash, Citation, memento and felicitation at next conference BITS Ranchi in 2016. The SIoT conference has been conducted to mark a difference to the existing system of bringing out a pre-conference volume of all the papers submitted to the conference. The organizing committee has decided to send all the accepted papers to the SRELS Journal of Information Management, the journal would do the required review process and publish the selected articles. The organizing committee felicitated CMD, Informatics India Pvt. Ltd., Bangalore and Dr. I R N Goudar at the end of the conference. Prof. Sangam gave concluding remark and Mr. Anand T Byrappa has rendered vote-of-thanks.
Conference Paper
Full-text available
The Internet is continuously changing and evolving. The main communication form of present Internet is human-human. The Internet of Things (IoT) can be considered as the future evaluation of the Internet that realizes machine-to-machine (M2M) learning. Thus, IoT provides connectivity for everyone and everything. The IoT embeds some intelligence in Internet-connected objects to communicate, exchange information, take decisions, invoke actions and provide amazing services. This paper addresses the existing development trends, the generic architecture of IoT, its distinguishing features and possible future applications. This paper also forecast the key challenges associated with the development of IoT. The IoT is getting increasing popularity for academia, industry as well as government that has the potential to bring significant personal, professional and economic benefits.
Article
Fog/edge computing has been proposed to be integrated with Internet-of-Things (IoT) to enable computing services devices deployed at network edge, aiming to improve the user’s experience and resilience of the services in case of failures. With the advantage of distributed architecture and close to end-users, fog/edge computing can provide faster response and greater quality of service for IoT applications. Thus, fog/edge computing-based IoT becomes future infrastructure on IoT development. To develop fog/edge computing-based IoT infrastructure, the architecture, enabling techniques, and issues related to IoT should be investigated first, and then the integration of fog/edge computing and IoT should be explored. To this end, this paper conducts a comprehensive overview of IoT with respect to system architecture, enabling technologies, security and privacy issues, and present the integration of fog/edge computing and IoT, and applications. Particularly, this paper first explores the relationship between Cyber-Physical Systems (CPS) and IoT, both of which play important roles in realizing an intelligent cyber-physical world. Then, existing architectures, enabling technologies, and security and privacy issues in IoT are presented to enhance the understanding of the state of the art IoT development. To investigate the fog/edge computing-based IoT, this paper also investigate the relationship between IoT and fog/edge computing, and discuss issues in fog/edge computing-based IoT. Finally, several applications, including the smart grid, smart transportation, and smart cities, are presented to demonstrate how fog/edge computing-based IoT to be implemented in real-world applications
Conference Paper
Online recommendation sites are valuable information sources that people contribute to, and often use to choose restaurants. However, little is known about the dynamics behind participation in these online communities and how the recommendations in these communities are formed. In this work, we take a first look at online restaurant recommendation communities to study what endogenous (i.e., related to entities being reviewed) and exogenous factors influence people's participation in the communities, and to what extent. We analyze an online community corpus of 840K restaurants and their 1.1M associated reviews from 2002 to 2011, spread across every U.S. state. We construct models for number of reviews and ratings by community members, based on several dimensions of endogenous and exogenous factors. We find that while endogenous factors such as restaurant attributes (e.g., meal, price, service) affect recommendations, surprisingly, exogenous factors such as demographics (e.g., neighborhood diversity, education) and weather (e.g., temperature, rain, snow, season) also exert a significant effect on reviews. We find that many of the effects in online communities can be explained using offline theories from experimental psychology. Our study is the first to look at exogenous factors and how it related to online online restaurant reviews. It has implications for designing online recommendation sites, and in general, social media and online communities.
This food startup uses Big Data to predict users' habits & cut wastage, e27
  • K Abudheen
Abudheen K, S. (no date) This food startup uses Big Data to predict users' habits & cut wastage, e27. Available at: https://e27.co/this-food-startup-uses-big-data-to-predict-usershabits-cut-wastage-20150603/ (Accessed: 21 April 2017).
A conceptual trust model for the Internet of Things interactions
  • A Arabsorkhi
  • M S Haghighi
  • R Ghorbanloo
Arabsorkhi, A., Haghighi, M. S. and Ghorbanloo, R. (2016) 'A conceptual trust model for the Internet of Things interactions', in 2016 8th International Symposium on Telecommunications (IST). 2016 8th International Symposium on Telecommunications (IST), pp. 89-93. doi: 10.1109/IS℡.2016.7881789.
Big Data, Gartner Available at: http://www.gartner.com/it-glossary/bigdata
  • Gartner
Gartner (2017a) Big Data, Gartner. Available at: http://www.gartner.com/it-glossary/bigdata (Accessed: 29 April 2017).