1. Introduction
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medical sciences, analysis of time series nowadays covers a wide range of real-life problems
including gene expression analysis and medical surveillance.
Notably, the availability of these enormous time series data has brought about huge advantages
in many areas. There is no doubt that proper management and analysis of time series has the
potential to become a driving force for innovation and decision making. If managed well, time
series can be used to unlock new sources of economic value, provide fresh insights into science
and hold governments to account. However, the easy management and analysis of these data
still pose a great challenge to scientists worldwide. Despite the abundance of tools to capture
and process all this information, issues like: ensuring data security, easy management of data,
analysis of huge datasets, optimizing response time for data retrieval, protecting privacy etc. are
still being faced as information is shared widely around the world.
Consequently, researchers, data analysts, statisticians and computer scientists are greatly
interested in finding the optimal processes and tools for managing and analyzing time series.
This has resulted in a large amount of research on new methodologies and technologies for
managing, analyzing, transforming, and visualizing time series data. Perhaps one major research
areas is in end-user development (Nardi & Miller, 1990). In order to validate the viability,
utility and interactivity of time series tools being rolled out, it is imperative to empower end
users as key players in the development of best management strategies for time series data.
Most programs today are written not by professional software developers, but by people with
expertise in other domains working towards goals for which they need computational support.
End users simply utilize the functions within an application to automate programming tasks. For
example, a teacher might write a grading spreadsheet to save time, or an interaction designer
might use an interface builder to test some user interface design ideas (Andrew, Robin, Laura,
& Alan, 2010).
Often, end-user programmers have a set of requirements which differs from those of
professional developers. End-user programmers generally program for themselves, a friend, or a
colleague. The end-user is the customer/user and is therefore programming to achieve a
personal goal, not programming to fulfill someone else’s; and there are no communication
issues. It is on the basis of this difference that researchers have begun to study end-user
programming practices and invent new kinds of technologies that collaborate with end users to
improve the quality of tools for data management (Burnett, 2009).
One notable application area of end user development is in spreadsheets. The spreadsheet
interface is easily adaptable for end user development. More so, the interface is accompanied
with a formula language which supports users with little or no formal training in programming.
This adaptability of spreadsheets for end user development derives from two properties of their
design:
Computational techniques that match users' tasks and that shield users from the low-
level details of traditional programming, and