AIRLINE DIGITAL CLICK STREAM EVENT
PROCESSING FOR ENRICHING THE
AIRLINE BUSINESS
Md. Alauddin
Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor,
Malaysia.
Ting Choo Yee
Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor,
Malaysia.
Ian Tan Kim Teck
Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Selangor,
Malaysia.
E-mail: alauddinm@gmail.com
Recepción: 02/08/2019 Aceptación: 24/09/2019 Publicación: 06/11/2019
Citación sugerida:
Alauddin, M., Choo Yee, T. y Kim Teck, I. T. (2019). Airline digital click stream
event processing for enriching the airline business. 3C Tecnología. Glosas de innovación
aplicadas a la pyme. Edición Especial, Noviembre 2019, 287-305. doi: http://dx.doi.
org/10.17993/3ctecno.2019.specialissue3.287-305
Suggested citation:
Alauddin, M., Choo Yee, T. & Kim Teck, I. T. (2019). Airline digital click stream
event processing for enriching the airline business. 3C Tecnología. Glosas de innovación
aplicadas a la pyme. Speciaal Issue, November 2019, 287-305. doi: http://dx.doi.
org/10.17993/3ctecno.2019.specialissue3.287-305
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
288
289
ABSTRACT
The new era of digital world with the rapid expansion of social network and mobile
applications created wider scope to expand airline industry for new way of promoting
their business. Due to several social media and other digital platforms, we need to
emphasize on target marketing/customer proling. Hence, to do target marketing,
a new web technology is created to collect each of the raw events of their web data
and mobile app data for tracking the way user is searching ights. In the proposed
method BigQuery is used to process huge volume of online customers’ data. The
proposed method is to understand the airline ecommerce online visitors eectively
by analysing the event data stream collected from various digital properties. The
obtained raw digital data consists of lot information with a semi-structured and it
needs to be cleansed before analysing it. So, the rst stage of proposed system is to
extract the data from various digital sources in real-time, then chose which data is
appropriate for analysing and nally extract the key insights to improve the airline
business. From the extracted variables, search patterns, the predictive models such as
ight search forecast, seat sales forecast and digital channel attribution models can
be developed.
KEYWORDS
Click stream processing, Big Query, Digital data processing, digital marketing, Data
Cleansing and Enrichment.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
288
289
1. INTRODUCTION
In recent years, most of the Asian airlines prime focus is on digital transformation
(O’Connell & Williams, 2005). The prime objectives of digital transformation are
to understand the online customer acquisition, digital channel attribution, online
customer segmentation, and their search trend. These are the most important
techniques to take right business action at right time to increase revenue. Most of
the airline industries have their own online and mobile based ecommerce platform,
it is possible to track and record their activities on the webpage as from which
webpage they have entered, when and what they search, where they drop o, what
they purchase, how frequently they book etc., (Klein & Loebbecke, 2000). These
visitor data can be for customer analytics like online customer prole, sales funnel to
understand at which point visitors drop o, are they price sensitive or not.
However, tracking and processing visitors’ raw events from the website logs data is
complicated because of the large volume of hit level data (One of the major Asian
airlines has about 15 million of online visitors per month, which generates roughly
3-5 billion events of unstructured or semi-structured web tracking data) (Ananthi,
2014). In this paper, the online digital click stream dataset is obtained from one of
the major Asian Airline system with 50 destinations. Each route is tracked with one
way and return ights for 30 days to 120 days. This paper mainly focus more on
the real-time digital data collection and pre-processing of the dataset for ight sales
prediction. The overall objective of the proposed work is that, the key variables are
selected from the extracted digital click stream data is to improve the airline business.
2. LITERATURE REVIEW
The growth of Internet around the world made airline business to change their way
of attracting the passengers (Singh & Jain, 2014). Also this digital era made to buy
tickets from anywhere in the globe at any time by comparing the dierent airlines. So
it is becoming very dicult to predict the ticket prices and attracting the passengers
becoming dicult with the inuence of many factors (Gillen & Lall, 2004). However,
data science showed a way to progress in this type of scenarios to study the patterns
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
290
291
and predict the behavior of the sales outcome. For example, it can be identifying the
correlation between seat prices of particular airlines and air trac delays. As per
recent surveys of (Forbes, 2008), it is noticed that for every minute of ight delay it
will aect the ticket prices about $1.5. Low cost airlines oer ticket pricing without
the baggage, food and beverages, which gives privileges to aord all common people
(Groves & Gini, 2013). Hofer, Windle and Dresner (2008) explained more details of
the how low cost airlines are dier from the other airlines. Lazarev (2013)
described
in detailed how fare variations can be inuenced in various time periods. Lazarev
designed very good model to predict optimum prices for low cost airlines to generate
almost 90% of the prot margin. In general, all the customers always think if earlier
booking ight fares might be less prices.
Based on the various studies on the airline business, the most important aspects to buy
tickets online in advance according to the user’s observation and their risk (Etzioni,
Tuchinda, Knoblock & Yates, 2003). The user who purchases their tickets online
should have a sense of control over the task they are performing over the Internet.
This helps to reduce the feeling of risk or fear associated with the possibility of:
making a mistake when making an airline booking online (that is, psychological risk);
not receiving their ticket or the ight not even existing (performance risk) (Brons,
Pels, Nijkamp & Rietveld, 2002). Several research papers described the promotions
on ticket prices, gift vouchers, airline points and upgrades, which playing indirectly
to attract the customers (Barrett, 2004; Gillen, & Lall, 2004). The majority of these
studies conclude that the incentives employed have a positive eect on airline ticket
purchase and repeat purchase and highlight that the eectiveness of the program
depends to a large extent on the particular incentive oered (Aviasales, n.d.). The
literature regarding the choice of Airlines has made it clear that both the benets
provided by frequent yer programs and air fares signicantly aect user’s choices
(Groves & Gini, 2013). Users who travel for business perceive the frequent yer
programs as more useful than other users. These authors even guarantee that business
travelers are willing to pay more in exchange for reducing access time, traveling with
top-ranked airlines, and traveling in a better class (O’Connell & Williams, 2005;
Sabre, 2015).
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
290
291
3. IMPLEMENTATION OF DIGITAL EVENT DATA
PROCESSING
In recent years, most of the people in the world entered towards digital era, which
increases the ecommerce transactions in a vast manner compared to the oine. Also
the power of digital world made people to reach the world from anywhere any time
through either social media, travel blogs or meta search engine. With these available
resources, the traveller’s can see dierent travel websites, travel blogs for price
comparison before they book their ight tickets. This open lot of opportunity for
the airlines to track the travellers search patterns and predict passengers’ behaviour
using predicting models. Besides, it is also possible to nd which online channel is
more eective for which airline routes and geo location for predicting the cost per
acquisition, which in turn save lot of advertisement costs. Further, the successful
tracking of all the digital data also enable the airlines to build sales funnel of digital
products, customer life time value calculation and other predictive modelling for
digital marketing.
3.1. DATA COLLECTION
To collect the online digital data and analyze its patterns, ve types of variables are
considered for better prediction of seat sales, which are:
• Visitor.
• Flight Search.
• Device.
• Channel.
• Transactions.
The transactional, operational data are extracted using various channels such as web,
mobile and tablets in the year 2016. The collection of digital data in real-time is so
complicated process, but with the evolution of Java scripts tagging framework, it is
possible to track each web page and its components based on visitor status on the
internet. The passenger activities such as which page they search, how much time
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
292
293
they spent on each webpage, how many clicks and scrolls on each page etc. Also, the
ecommerce related information such as add to cart, product related information and
ecommerce transaction details etc. As the ight sales digital web data is very big and
complex, the data collected, cleansed and processed using cloud technology. The
implementation of digital analytics will help marketing to monitor the load factor
(%) for future ights and how traveler is choosing origin hub to destination hub and
other connecting hubs using y through (transit). Figure 1 shows the detailed block
diagram of the airline data collection from various sources and its predictive model.
Figure 1. Airline digital data processing architecture.
Airline travel visitors search ight from dierent devices such as desktop, mobile devices
and tablets. Therefore collecting the data from dierent devices is bit complicated, so
it is necessary to consider each digital properties carefully. To collect the digital data
(raw data) from various sources, a renowned tracking framework (The java script which
is modication of Google tracking framework) is used. After collecting the data and tracking
the gathered data, the user activity sends to the server for reporting and further
analysis. The system uses dierent technologies to create data hits according to the
types of digital properties. Hence, a new custom code is implemented for tracking
web and mobile app users’ activity. The proposed custom code also identies the
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
292
293
new users and returning users, which provides the more information to x the seat
price dynamically. Finally, the custom code is implemented for capturing the business
specic information such as Flight Search Origin, Flight Search Destination, and
Departure Date etc. Also, the web server is tracked to receive HTTP request, which
gives the details of the airline customers searching patterns. From the webserver log
the customers details (such as, computer info, the Location, hostname, the browser
type, and language they are browsing etc.,) are extracted.
In the proposed research, BigQuery is used to process high volume of customers’
digital data. BigQuery is a RESTful web service that enables interactive analysis
of massively large datasets working in conjunction with Google Storage. It is an
Infrastructure as a Service (IaaS) that may be used complementarily with Map
Reduce. BigQuery is used to process the raw data to further level. After exporting
each digital properties as raw tables, which are available in BigQuery as multiple
daily tables. BigQuery uses SQL syntax to process the raw data. Figure 2 shows the
airline ight search data processing ow. Figure 3 shows the airline online trac and
search data processing ow from all airline digital properties in a daily aggregation.
After tracking for capturing the web and mobile digital properties and the listed
attributes, the captured data is exported to BigQuery on a periodic basis.
Figure 2. The block diagram of Airline Flight Search data processing ow.
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
294
295
In general, the open source tracking code retrieves web page data as follows:
• A browser requests a web page that contains the tracking code.
• A JavaScript Array is created and tracking commands are pushed onto the
array.
• A <script> element is created and enabled for asynchronous loading (loading
in the background).
• The ga.js tracking code is fetched, with the appropriate protocol automatically
detected. Once the code is fetched and loaded, the commands on the array
are executed and the array is transformed into a tracking object. Subsequent
tracking calls are made directly to the server.
• Loads the script element to the DOM.
• After the tracking code collects data, the GIF request is sent to the analytics
database for logging and post-processing.
A GIF request can be classied into few types. Table 1 shows various types of GIF
request. In each of these cases, the GIF request is identied by type in the utmt
parameter. In addition, the type of the request also determines which data is sent
to the Analytics servers. For example, transaction and item data are only sent to the
Analytics servers when a purchase is made. User, page, and system information is
only sent when an event is recorded or when a page loads and the user-dened value
is only sent when the _setVar method is called.
Table 1. GIF request types.
Request Type Description Class
Page A web page on your server is requested Interaction
Event
An event is triggered through Event Tracking that
you set up on your site
Interaction
Transaction A purchase transaction occurred on your site Interaction
Item
Each item in a transaction is recorded with a GIF
request
Interaction
Var
A custom user segment is set and triggered by a
user
Non-interaction
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
294
295
Figure 3. Airline online trafc and search data processing ow.
Raw web tracking data Processing: The volume of one year raw data is about
six Terabyte. So, the rst step used BQ SQL query to fetch the hits level data from
BQ raw daily tables.
Data Cleansing and Enrichment: Raw tracking data have date format issues
such as hit_timestmap in one format, the date extracted from page path URL has
another format and custom dimension has dierent format. Therefore, all types of
dates are converted in one standard format with same time zone. There are missing
values of trac information, ight search information, geo information, transaction
information. To handle the missing values, rst a metadata reference table have been
created from other available attributes. Then the missing values are enriched using
metadata tables. Also, the dierent digital properties captured with same information
but dierent attributes name. Those need to be merged into one column. Figure 4
shows the BigQuery processing ow to predict the sector levels.
Figure 4. BigQuery processing ow to predict the different sectors data.
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
296
297
All digital data is stored in google storage which contains all hit level records of
the visitor’s clickstream. This data has been ltered to get the hits which give user
interactions of ight search. The ‘hit-type’ lter has been set to ‘EVENT’ or ‘PAGE’.
This will lter out all interaction hits of ight-search page view or ight-search event
action such as click on search button. At the same time, hit-type equals APPVIEW
lters all the hits from Mobile and Tablet App (iOS and Android). These lters
exclude lots of impression and other irrelevant hits records. It also helps to reduce
the data volume that we must process in next stage. After that, the landing hits for
the web has been ltered. This provides rst hit and search hits only. It also ensures
the exclusion of all other activity hits after searches such as passenger details page,
add-on page, conrmation page and payment page. After that next challenge is to
lter only search hit pages from the web, mobile and tablet a The search page will
be identied by page-path mapping for web and screen name for mobile and tablet
a However, there are dierent versions of web application and mobile app release
with a website revamp and new version release for a mobile a Thus, the page-path
and mobile screen name are not constant. To overcome this limitation all dierent
search identier, need to be collected for each release over time to create a reference
mapping table. This table could be used to identify all search hits from all devices.
Sometimes customer searches non-operational ight route. Thus, all ight search
has been examined to verify the searched route by the customer. All non-operational
ight route searched by the customer have been ltered to avoid any misleading data
for the nal training set.
Digital data aggregation
With the clean, structured and quality data produced after data cleansing, enrichment
and transformation, aggregation can now be performed to get desired data set.
Algorithm 1 shows the high-level process of aggregating the digital data.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
296
297
Algorithm 1: generate-aggregated digital data set
Input:
Output:
for d in (ClickStreamRecords) do
1. search_timestamp ← Transform UNIX to timestamp (concat(
.visitStartTime,
date))
2. visitID ← concat (sessionId, visitId)
3. visitorID ← Extract (
.fullVisitorId)
4.
← Extract (Max(CustomDimension.index, CustomDimension.value)
by iterating each items))
end for
for d in (
) do
uniqueUsers ← COUNT (Distinct (
.visitorID) by Routes)
uniqueSearch ← COUNT (Distinct (
.sessionID) by Routes)
NoOfUsers ← COUNT (
.sessionID)
end for
All digital platform (Web/Mobile/Tablet) data has been merged to make a one single
data source. Since all the digital data are in the same structure, a UNION operation
in BigQuery can merge multiple datasets of the same structure. This merged data
table is named as ‘clickStreamRecords’. Algorithm 3 takes this data as input. First
step of the algorithm is to extract visitId and visitorId of the customer by hourly,
daily, weekly and monthly basis and stored as D
FlightSearch
.
After that, data has been aggregated to get the no. of ight, no. of unique user
perform ight search and no. of total search as well as group by each selected route
(origin and destination), search date and departure date. Furthermore, search-
lead-days have been calculated by subtracting search-date from departure-date.
This will compute how many days before the departure, customer searched for the
ight. Output of this algorithm has been stored as D
uniqVisitorByRoute
, D
uniqFlightSearchByRoute
, and
D
NoOfFlightSearchByRoute
. Aggregated nal dataset sample has been shown in Table 2.
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
298
299
Table 2. Sample of digital data.
Attributes name Examples
fullVisitorId 1527445791
visitId 1527445791
SearchedOrigin DXB
SearchedDestination HKT
SearchedDepartureDate 2018-05-21
SearchReturnDate 2018-05-28
unique_search 6
NumberSearches 10
4. RESULTS AND DISCUSSION
The dierent datasets extracted from the total roll up are:
Visitor landing dataset with trac source information: From which trac source
visitors performs the rst hit at website and then what they do after landing to the
website. Visitors can come from dierent types of online channel such as Paid Search,
Organic Search, Paid Social, Meta Search, Direct etc. And after they renter into
same airline webpage, it tracks the search ights as this increase the probability to
purchase tickets. However, user might nd irrelevant after landing to website hence
drop o or visits web check-in, member sign o and other promotional pages.
Visitor Flight Search dataset: Fight Search dataset have multiple critical attributes
such as Unique search visitors, Unique Search by Route, No of Total Search by
Route, and other attributes
Ecommerce transaction dataset: which gives the money transactions on the seats
bookings.
The reports produced from nal stage of aggregated dataset is shown in Figure 5.
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
298
299
Figure 5. Digital airline Website tracking analysis report.
The digital airline website tracking analysis results shown in Figure 5 gives the
summary of how many visitors visits each day and how many users log on to the
website second time. Also, it shows how many numbers of sessions are in active, how
long the user sessions were active. From these analysis, it is noticed that, based on
the users searching patterns ight fares and seats could be decided. Few routes digital
variable data have analyzed based on the seats sale using the correlation analysis and
identied the best and worst routes, which is shown in Table 2.
Table 3. Correlation analysis results.
Worst Case Route Best Case Route
Total seats sold Total seats sold
Total seats sold 1.000 Total seats sold 1.000
Total unique visitors 0.179 Total unique visitors 0.576
Total unique search 0.216 Total unique search 0.609
Total number of
search
0.242
Total number of
search
0.572
Total unique
sessions
0.198
Total unique
sessions
0.593
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
300
301
From the Table 3 results, it is observed that, digital variables can be a strong
descriptor in some routes for seat sales. This shows the potential value in including
these digital variables into the model in addition to the obvious transactional variable
and operational variables to get more information. It is clearly noticed that users
meta and paid search rates are higher compared to the direct search rates. It is also
showed that meta search rate is higher in booking also. From these analysis it is
observed that, users meta search is using to book ight seats. From all the digital
variable data, transactional data and operational data, the seat sales have predicted,
which is shown in Figure 6.
Figure 6. Seat sales forecast from various data variables data.
The forecast results showed in Figure 6 are part of the analysis. From the graph
shown in Figure 6 that, the predicted values almost 6.5 to 9% deviation from the
actual values. To predict accurately, hybrid models with ANN and ARIMA models
are going to be implemented in further research works.
5. CONCLUSION AND FUTURE WORK
In this paper, the main approach used for selecting the important variables in ight
sales forecast of each day on the route level. In this, for events tracking and web data
tracking Java script is used. From the digital click stream data, the most prominent
ve selected variables were extracted to nd visitors trac, ight search transactions,
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
300
301
device data and channel data. These ve selected variables data will be used to build
models for predictive analytics such as Seat Sales Prediction, Revenue Optimization
with Digital and Transaction data, Channel Attribution Model, Customer Life time
value, which could bring tremendous business value. The proposed correlation
analysis of the extracted variables, the model produced around 7% and 9% error rate
when forecasting 30 days and 60 days ahead respectively. This paper discussed only
the requirements and design constraints of the dynamic models. In our next paper,
the dynamic predictive models will be described in detail with the suitable analysis
results to predict the seat sales forecast dynamically according to the extracted real
time digital data.
REFERENCES
Singh, A. P., & Jain, R.C. (2014). A Survey on Dierent Phases of Web Usage
Mining for Anomaly User Behaviour Investigation. International Journal of Emerging
Trends & Technology in Computer Science, 3(3), 70-75. Retrieved from: https://www.
ijettcs.org/Volume3Issue3/IJETTCS-2014-06-03-066.pdf
Ananthi, J. (2014). A Survey Web Content Mining Methods and Applications for
Information Extraction from Online Shopping Sites. International Journal of Computer
Science and Information Technologies, 5(3), 4091-4094. Retrieved from: https://www.
semanticscholar.org/paper/A-Survey-Web-Content-Mining-Methods-and-for-
from-Ananthi/36613a9e89bf3efda507568b54295b92f2f8ad23
Aviasales. (n.d.). Aviasales API. Retrieved August 17, 2015, from: http://www.
aviasales.ru/API
Barrett, S. D. (2004). How do the demands for airport services dier between full-
service carriers and low-cost carriers? Journal of Air Transport Management, 10(1),
33-39. Retrieved from: https://www.academia.edu/2422292/How_do_the_
demands_for_airport_services_dier_between_full-service_carriers_and_low-
cost_carriers
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
302
303
Brons, M. R., Pels, E., Nijkamp, P., & Rietveld, P. (2002). Price elasticities of
demand for passenger air travel: a meta-analysis. Journal of Air Transport Management,
8(3), 165-175. doi: https://doi.org/10.1016/S0969-6997(01)00050-3
Etzioni, O., Tuchinda, R., Knoblock, C. A., & Yates, A. (2003). To buy or not
to buy: mining airfare data to minimize ticket purchase price. Proceedings of the ninth
ACM SIGKDD international conference on Knowledge discovery and data mining, 119-128.
Retrieved from: https://www.isi.edu/integration/papers/etzioni03-kdd.pdf
Forbes, S. J. (2008). The eect of air trac delays on airline prices. International journal
of industrial organization, 26(5), 1218-1232. Retrieved from: http://recherche.enac.
fr/~steve.lawford/teaching_papers/econometrics2_2019/papers/forbes08.pdf
Francis, G., Fidato, A., & Humphreys, I. (2003). Airport-airline interaction:
the impact of low-cost carriers on two European airports. Journal of Air Transport
Management, 9(4), 267-273. doi: https://doi.org/10.1016/S0969-6997(03)00004-
8
Gillen, D., & Lall, A. (2004). Competitive advantage of low-cost carriers: some
implications for airports. Journal of Air Transport Management, 10(1), 41-50. doi:
https://doi.org/10.1016/j.jairtraman.2003.10.009
Groves, W., & Gini, M. (2013). An agent for optimizing airline ticket purchasing.
Proceedings of the 2013 international conference on Autonomous agents and multi-
agent systems, 1341-1342. Retrieved from: https://www.researchgate.net/
publication/262172314_An_agent_for_optimizing_airline_ticket_purchasing
Hofer, C., Windle, R. J., & Dresner, M. E. (2008). Price premiums and low cost
carrier competition. Transportation Research Part E: Logistics and Transportation Review,
44(5), 864-882. doi: https://doi.org/10.10.1016/j.tre.2007.03.004
Klein, S., & Loebbecke, C. (2000). The transformation of pricing models
on the web: examples from the airline industry. 13th International Bled Electronic
Commerce Conference, 19-21. Retrieved from: https://pdfs.semanticscholar.
org/3112/430cbe6730d9c3eb559b83ab373bb913ec19.pdf
Edición Especial Special Issue Noviembre 2019
DOI: http://dx.doi.org/10.17993/3ctecno.2019.specialissue3.287-305
302
303
Lazarev, J. (2013). The welfare eects of intertemporal price discrimination: an
empirical analysis of airline pricing in US monopoly markets. Retrieved from:
http://www.johnlazarev.com/Lazarev_JMP.pdf
O’Connell, J. F., & Williams, G. (2005). Passengers’ perceptions of low cost
airlines and full service carriers: A case study involving Ryanair, Aer Lingus, Air
Asia and Malaysia Airlines. Journal of air transport management, 11(4), 259-272. doi:
https://dx.doi.org/10.1016/j.jairtraman.2005.01.007
Sabre. (2015). APIs. Get travel information on demand with our REST and SOAP APIs.
Retrieved August 17, 2015, from: https://developer.sabre.com/docs
AUTHORS BIOGRAPHY
Md. Alauddin is currently Masters Student in the Faculty of
Computing and Informatics, Multimedia University, Cyberjaya,
Malaysia. He is a Computer Science and Engineering Graduate
from Khulna University of Engineering and Technology with
major of Software Engineering. His research interest mostly on
BigData, Machine Learning and Data Engineering.
Dr. Choo-Yee Ting is currently holding Associate Professor
in the Faculty of Computing and Informatics, Multimedia
University, Cyberjaya, Malaysia. In the year 2002, Choo-Yee
Ting is awarded the Fellow of Microsoft Research by Microsoft
Research Asia, Beijing, China. In 2003, he received research
fellowship from Rotary Research Foundation, Rotary Club of
Kuala Lumpur Diraja, Malaysia. He has been involving himself
in research projects funded by MOSTI, Malaysia and Industries.
He is also certied in Microsoft Technology Associate (Database)
and IBM DB2 CDA.
3C Tecnología. Glosas de innovación aplicadas a la pyme. ISSN: 2254–4143
304
305
Dr. Ian Tan Kim Teck is currently holding senior lecturer in the
Faculty of Computing and Informatics, Multimedia University,
Cyberjaya, Malaysia. Ian Tan Kim Teck is graduated with
a Doctor of Philosophy (Ph.D.) from Multimedia University,
Malaysia in the area of Operating Systems’ schedulers. He
did his Master of Science Degree in Parallel Computers and
Computation, from University of Warwick, United Kingdom in
1993 and a Bachelor of Engineering Degree and Associate of
City and Guilds Institute in Information Systems Engineering,
from Imperial College London, United Kingdom in 1992. He
is also Novell Certied Linux Administrator (NCLA), Novell
Certied Linux Professional (NCLP), member of IEEE and
member of ACM. His area of research interest is primarily in
systems; from operating systems process scheduling on multicore
systems, ecient network data transfers, to systems and network
security.
