=Paper=
{{Paper
|id=Vol-2841/BigVis_4
|storemode=property
|title= Real-time visualization of profiling metadata upon data insertions
|pdfUrl=https://ceur-ws.org/Vol-2841/BigVis_4.pdf
|volume=Vol-2841
|authors=Loredana Caruccio,Stefano Cirillo,Vincenzo Deufemia,Giuseppe Polese
|dblpUrl=https://dblp.org/rec/conf/edbt/CaruccioCDP21
}}
== Real-time visualization of profiling metadata upon data insertions==
https://ceur-ws.org/Vol-2841/BigVis_4.pdf
Real-time visualization of profiling metadata
upon data insertions
Loredana Caruccio, Stefano Cirillo, Vincenzo Deufemia, Giuseppe Polese
Department of Computer Science
University of Salerno
Fisciano (SA), Italy
{lcaruccio,scirillo,deufemia,gpolese}@unisa.it
ABSTRACT Many data cleaning approaches exploit profiling metadata, that
Data-intensive processes must deal with the problem of monitor- permit to describe properties that must hold on data, and conse-
ing the quality of data. To this end, metadata can be exploited quently they can be used to monitor the quality of the data them-
in order to highlight errors and support the cleaning of data. selves. Examples of metadata are unique column combinations,
In this paper, we present a novel DBMS plugin for validating functional dependencies, denial constraints, and so on. More-
profiling metadata during data insertions, aiming to assist the over, nowadays the exploitation and validation of these metadata
user in a-priori checking the quality of data being inserted into are enhanced thanks to recent data profiling algorithms, which
a database. It has been implemented within the MySQL Work- enable their automatic discovery from “big” datasets [1, 4, 10, 21].
bench client and is able to intercept and validate data insertion DataBase Management Systems (DBMSs) have been recog-
queries. More specifically, using such a plugin it is possible to nized as a useful tool enabling easy interactions between the user
verify in real-time whether the data to be inserted into a database and the stored data. In particular, they permit to inquire data
instance will produce some violations on specific metadata, such with little effort [2]. They can also be used to perform data fusion
as unique column combinations or functional dependencies. A activities, after schema matching processes [5]. In such scenarios,
user study highlighted the effectiveness of the proposed plugin it would be desirable that the validation of profiling metadata
by also emphasizing its strengths and weaknesses. be managed by users through the definition of some constraints.
However, to the best of our knowledge, DBMSs do not permit to
verify the validity of metadata, such as fd’s satisfiability, on the
KEYWORDS
current database instance, and/or verify whether some of them
Data profiling, Functional dependencies, Data insertions, MySQL could be invalidated upon data insertion operations.
workbench In this paper we introduce a novel visual tool named indi-
tio (INteracting with metaData during data InserTIOns), which
permits to monitor in real-time, and possibly validate, specific
1 INTRODUCTION profiling metadata upon data insertion operations. In particu-
lar, the tool has been implemented as a plugin of the graphical
Data quality is becoming a vital activity in most application do- client MySQL Workbench, and it enables the user to intercept
mains. The presence of errors and inconsistencies in the data data insertion queries, in order to validate specified (or uploaded)
drastically reduce the value of data and processing capabilities metadata before the insertion is committed. In this way, a user
on them [13]. Nowadays, data-intensive systems are involved can check the correctness of the data s/he is planning to insert,
in everyday life and enable complex analysis based on the data and decide either to force the insertion nonetheless the data, or
they process. Examples are data warehousing and data mining try to fix them. Consequently, inditio manages the specification
systems, through which statistics on data, classification tasks, (uploading) of metadata and the visualization of validation re-
and in general novel insights can be generated. Data is a valuable sults together with values inducing possible violations. Moreover,
asset for customers, companies, and governments, which yields different statistical counters and visual components enable users
their quality to have a fundamental role in data governance mod- to have an overview of the general impact of data insertions
els [12]. Nevertheless, the possible presence of dirty data might on the considered metadata. The current implementation of the
produce negative effects on their results. To this end, big efforts plugin is able to manage unique column combinations (uccs)
have been devoted to cleaning data before employing them in and functional dependencies (fds), but it could be extended to
data-intensive processes. In fact, many approaches to automat- include other kinds of profiling metadata.
ically perform cleaning tasks, such as de-duplication, missing In general, the goal that guided such work is threefold: i)
value imputation, and entity resolution represent a landmark for improve the quality of data by driving users in the data insertion
guaranteeing a proper drawing of the reality through the data de- process, ii) sensitize more users to the data profiling concepts, and
scribing it. Such approaches permit to automate and/or optimize iii) support an implicit analysis on the significance of metadata
data preparation tasks at semantic-level, which would otherwise from domain experts.
require bigger efforts, tedious and long lasting pre-processing The paper is organized as follows. Section 2 describes ap-
activities. In fact, it is well-known that data scientists spend most proaches to data quality and data profiling tools. Section 3 presents
of their time in data preparation tasks [18]. the theoretical foundations of considered profiling metadata. Sec-
tion 4 presents inditio, whereas Section 5 reports a user study
© 2021 Copyright for this paper by its author(s). Published in the Workshop Proceed-
we performed to analyze its effectiveness and usability. Finally,
ings of the EDBT/ICDT 2021 Joint Conference (March 23–26, 2021, Nicosia, Cyprus) summary and future directions are included in Section 6.
on CEUR-WS.org. Use permitted under Creative Commons License Attribution 4.0
International (CC BY 4.0)
2 RELATED WORK kinds of properties, ranging from statistics, domain cardinalities,
Metadata have been recognized as a fundamental tool for guar- frequent patterns to clusters, outliers, and data dependencies. All
anteeing good levels of quality to the extension of data, mainly of them might be exploited in several advanced database oper-
due to their involvement in data cleaning processes [19]. Among ations, such as query optimization, data cleaning, and so forth.
the different data cleaning activities, it is possible to find: schema The proposed plugin considers two specific types of metadata,
mapping, de-duplication, classification and mastering, spotting e.g., Unique Column Combination (ucc) and Functional Depen-
errors and violations (e.g., outliers), repairing incorrect values, dency (fd), which are described in what follows, after a brief
missing value imputation [16]. introduction to relational databases.
Data cleaning activities have also been included in several A relational database schema R is defined as a collection of
data preparation commercial tools [15]. Two of the main frame- relation schemas (𝑅1 ,. . ., 𝑅𝑛 ), where each 𝑅𝑖 is defined over a
works that can be used to perform data cleaning are: Llunatic set 𝑎𝑡𝑡𝑟 (𝑅𝑖 ) of attributes (𝐴1 ,. . ., 𝐴𝑚 ). Each attribute 𝐴𝑘 has as-
[14] and HoloClean [22]. The first represents one of the first uni- sociated a domain 𝑑𝑜𝑚(𝐴𝑘 ), which can be finite or infinite. A
form frameworks for data-cleaning activities, which also intro- relation instance (or simply a relation) 𝑟𝑖 of 𝑅𝑖 is a set of tuples
duced novel semantics and notions in the data cleaning research (𝑡 1, . . . , 𝑡𝑝 ) such that ∀ 𝐴𝑘 ∈ 𝑎𝑡𝑡𝑟 (𝑅𝑖 ) 𝑡 𝑗 [𝐴𝑘 ] ∈ 𝑑𝑜𝑚(𝐴𝑘 ), where
area. The latter relies on classical concepts, such as integrity 𝑡 𝑗 [𝐴𝑘 ] denotes the projection of 𝑡 𝑗 onto 𝐴𝑘 . A database instance
constraints and external data sources, but also on statistical prop- 𝑟 of R is a collection of relations (𝑟 1 ,. . .,𝑟𝑛 ), where 𝑟𝑖 is a relation
erties of the input data. The main goal of HoloClean is to auto- instance of 𝑅𝑖 , for 𝑖 ∈ [1, 𝑛].
matically generate a probabilistic program that performs data In the context of relational databases, one of the main property
cleaning. is represented by candidate keys. They permit to define possible
Data cleaning and metadata verification activities have also tuple identifiers of a relation instance since no repetition in value
been included into, mainly commercial, data governance suites, combinations are allowed. They can be effectively identified by
such as Talend [23] and IBM InfoShere [11]. Among the many exploiting unique column combinations (uccs).
advanced database operations, these suites permit to validate and ucc definition. A ucc over a relation schema 𝑅 is a sets of
possibly to graphically check data quality constraints. attributes 𝐾 ⊆ 𝑎𝑡𝑡𝑟 (𝑅) such that given an instance 𝑟 of 𝑅, for
The possibility of exploiting metadata is given by the existence every pair of tuples (𝑡 1 , 𝑡 2 ) in 𝑟 then 𝑡 1 [𝐾] ≠ 𝑡 2 [𝐾].
of algorithms capable of discovering them from big data sources Another relevant property of a relational database is repre-
[1, 4, 10, 21]. Metadata discovery processes are also provided sented by functional dependencies (fds), which are used to im-
by effective platforms for data profiling, such as the Metanome prove the quality of database schemas and to reduce manipulation
project [20], which embeds several algorithms to automatically anomalies.
discover complex metadata, including functional and inclusion fd definition. An fd over a database schema R is a statement
dependencies; and Metacrate [17], which permits the storage 𝑋 → 𝑌 (𝑋 implies 𝑌 ) defined between two sets of attributes
of different meta-data and their integration, enabling users to 𝑋, 𝑌 ⊆ 𝑎𝑡𝑡𝑟 (R), such that, given an instance 𝑟 of R, 𝑋 → 𝑌 is
perform several ad-hoc analysis. satisfied in 𝑟 if and only if for every pair of tuples (𝑡 1 , 𝑡 2 ) in 𝑟 ,
Nevertheless, since automatic discovery processes could out- whenever 𝑡 1 [𝑋 ] = 𝑡 2 [𝑋 ], then 𝑡 1 [𝑌 ] = 𝑡 2 [𝑌 ]. 𝑋 and 𝑌 represent
put many metadata, some novel proposals started to visually the Left-Hand-Side (LHS) and Right-Hand-Side (RHS) of the fd,
manage the complexity related to an effective visualization of respectively.
metadata holding on a given dataset, by using novel metaphors In general, an fd is said to be non-trivial if and only if 𝑋 ∩𝑌 = ∅.
for representing metadata at different levels of detail [9]. More- Moreover, an fd is said to be minimal if and only if there is no
over, other proposals allow users to explore how metadata change attribute 𝐵 ∈ 𝑋 such that 𝑋 \𝐵 → 𝑌 holds on 𝑟 .
over time, and to compare the results obtained among different
time-slots [6], or to represent how discovery results change into 4 INDITIO: A MYSQL WORKBENCH PLUGIN
the search space [7]. In this section, we describe inditio a MySQL Workbench plugin
All of the above-mentioned approaches try to repair/analyze capable of validating profiling metadata upon the insertion of new
available datasets aiming at improving their quality and/or de- tuples in a database. MySQL Workbench1 is the official graphical
tect/visualize possible holding metadata. On the contrary, the client of MySQL [3], which covers many functionalities, among
proposed inditio plugin aims at improving the quality of data, which the most important are the possibility 𝑖) to graphically
trying to directly make users, i.e., who define data, aware of the create models of database schemas, 𝑖𝑖) to edit tables, columns,
possible errors that they can introduce into database instances. indices, and so forth, and 𝑖𝑖𝑖) to create and manage connections to
For this reason, we propose a tool that could be properly inte- database servers, along with providing the capability to execute
grated into a DBMS. In this way, users and/or domain experts can SQL queries using the built-in SQL Editor (see Figure 1).
approach this kind of concepts by simply using the environment inditio has been implemented by exploiting the libraries ex-
they are familiar with. In other words, inditio aims at including posed from Oracle, to directly interact with the main components
data profiling discovery results within DBMSs, facilitating their of the software. Thanks to the most recent versions of the MySQL
interpretation to users through a plugin that also includes several Workbench source packages, it was possible to develop the pro-
visual components. To the best of our knowledge, in the litera- posed plugin using Python 2.7. Although this is not the latest
ture, there are no other DBMS integrated plugins considering the version of this programming language, it is the only supported
interaction among data definition statements and metadata. version by MySQL Workbench. In fact, it is important to notice
that MySQL Workbench has been developed by exploiting C++ li-
3 DATA PROFILING braries, and supports Python libraries only by means of a wrapper
that is able to translate Python code into C++ code. This makes
Collecting metadata from big datasets is the goal of the data
profiling research area [19]. Profiling metadata refer to many 1 https://www.mysql.com/it/products/workbench/
Finally, it is always possible to “Copy Selected Metadata” as text
by means of a specific button.
Notice that metadata are described through letters or numbers,
e.g., alias, in order to identify attributes. This facilitates users in
focusing on attributes and/or in defining new metadata. In fact,
possible long (or inappropriate) attribute names could confuse
the user. However, inditio provides a suitable form to show the
mapping between attribute names and their associated alias (see
Figure 1: The MySQL Workbench SQL Editor. Figure 2(c)).
All selected metadata can be validated by clicking on “Run
Validation”, which triggers the execution of a validation module
MySQL Workbench a real challenge for external developers look-
whose aim is to check if the new tuples violate the selected
ing to extend its functionalities, since it is not always possible to
metadata. According to the validation process, each metadata
install new modules. For this reason, the most popular currently
can be classified in one of the following categories:
available plugins created by external developers 2 are divided
into two categories: 𝑖) MySQL database documentation genera- • Valid Metadata, when the new tuples do not produce any
tion; and 𝑖𝑖) MySQL Workbench export. Both types of plugins violation;
work in batch and are bound to execute a single operation. On • Not Valid Metadata, when the new tuples entail at least
the contrary, inditio fully exploits the functionalities of MySQL one violation; or
Workbench as described in the following section. • Impossible to validate, when the metadata cannot be vali-
dated. This occurs when the user introduces errors in the
4.1 Interacting with metadata upon data metadata, such as when the attribute names do not exist
in the considered database.
insertions
inditio is able to intercept data insertion queries provided by Example 4.1. Let us consider a database storing smartphone
users into the SQL Editor. Due to the possible big number of characteristics. Figure 2(e-g) show validation results of the con-
errors that can be introduced during the insertion of new tuples, sidered fds (Figure 2(d)) according to the new tuples the user is
DBMSs should enable users to visualize profiling metadata that planning to insert (see Figure 2(a)). In particular, three out of six
newly inserted data could possibly invalidate, by also giving the fds are valid, two are invalid, and one cannot be validated. In
possibility to interact with them. For this reason, it should be nec- fact, K,M → D includes attribute M that does not appear in the
essary to evaluate such metadata upon data insertion operations. considered database. Instead, C → E, i.e., ram → display_ppi, is
To this end, inditio extends MySQL Workbench functionalities invalidated if the new tuples are inserted.
by enabling users to validate uccs and fds upon the insertion of
new tuples. In general, the impact of the new tuples on the considered
Figure 2 shows the general visual interface of inditio. In gen- metadata is summarized by inditio in a new form, named re-
eral, it permits to evaluate the impact of new data on a set of port form, shown in Figure 4. This form graphically shows the
holding metadata. Thus, it enables users to visualize the new tu- percentage of validation/invalidation produced on the selected
ples being inserted (Figure 2(a)), the metadata to validate (Figure metadata by the tuples the user is planning to insert. Moreover,
2(b-d)), and the results of the metadata validation process (Figure the report form ranks database attributes in descending order ac-
2(e-h)). Moreover, inditio also provides several functionalities cording to the number of invalidated metadata containing them.
enabling users to interact with both SQL statements and meta- More specifically, the form represents this kind of information
data, as described in the following. A demonstration video of according to the type of metadata, i.e., by splitting the informa-
inditio is available on YouTube3 . tion about invalidation on LHS and RHS when considering fds
(see Figure 4(a)).
4.1.1 Analysis of metadata. The main novelty introduced by
inditio is the possibility to evaluate some metadata directly into Example 4.2. Figure 4(a) shows the fd validation report for
the MySQL Workbench. In fact, a user can upload uccs or fds by the validation results represented in Figure 2. In particular, the
selecting the type of metadata s/he plans to monitor. In particular, form shows that the impact of invalidations is 33% of the ana-
the metadata will be directly uploaded through a default file, but lyzed metadata. Moreover, among the attributes involved in the
they can be changed by selecting a new JSON file (Figure 2(b)). invalidated metadata, attribute C (e.g., ram) is involved in two
The uploaded metadata are shown in the middle form (Figure invalidated fds; whereas attributes E (e.g., cpu) and D (e.g., dis-
2(d)), which is customized according to the type of metadata the play_ppi) are involved in one invalidated fd. This could suggest
user selects. For instance, fds are divided into LHS and RHS, each to verify the values of attributes ram, cpu, and display_ppi of the
containing some attributes, in order to graphically visualize the new tuples.
implication property. Instead, the ucc customized form visualizes 4.1.2 Interacting with data insertions. inditio not only en-
each metadata by considering a single group of attributes (Figure ables users to visualize the impact of new tuples on holding
3). Aside from the metadata uploaded via file, a user can always metadata, but it also permits them to interact with the new data.
add new metadata. Moreover, through this form it is possible to First of all, INSERT INTO statements can always be modified
select which metadata must be considered during the validation. within the inditio interface (see Figure 2(a)), triggering subse-
Indeed, each metadata can be selected by means of the check quent validation processes with modified tuples. As said above,
box, and/or by using the “Select All” or “Unselect All” buttons. inditio freezes the execution of INSERT INTO statements while
2 https://github.com/cciro94/MySQL-Workbench-Plugins verifying the possibility to correct values being inserted so as
3 https://youtu.be/u03Vftge8pA not to invalidate holding metadata. Nevertheless, inditio always
Figure 2: The inditio visual interface.
results represented in Figure 2. In particular, the form shows
that four specific values on attribute C (e.g. ram), i.e. 1Gb, 2Gb,
3Gb, 512mb, each implies different values of attribute E (e.g. dis-
play_ppi). Moreover, it is also possible to see that the value 2Gb
is the one implying the highest number of distinct values. In-
stead, Figure 5(a) shows that for the ucc K,E,L (e.g. id_brand,
display_ppi, id_os) there are six specific value combinations in-
ducing duplicate values. In general, this form could suggest to
Figure 3: Visualization of uccs. correct values on the new tuples concerning attributes involved
in the considered violated metadata, or to exclude the metadata
from the validation process.
gives users the possibility to overlook possible violations of meta-
data and to force the execution of data insertion operations by 5 USER STUDY
means of the “Execute Statements” button (see Figure 2(a)).
The user study presented in this section aims to show that inditio
On the other hand, one of the main goals of inditio is to help
makes metadata validation a simple and effective process for
users in correcting possible errors. To this end, after a validation
improving data quality.
process (i.e., by clicking “Run Validation”), a user can visualize
data yielding violations by interacting with the “Not Valid Meta- 5.1 Method
data” form (see Figure 2(f)). More specifically, by clicking on any
metadata in such form, inditio shows a violation detail form, as We recruited 86 students majoring in Computer Science who
shown in Figure 5. In particular, concerning fds, apart from the just attended the Fundamentals of database. We also recruited 3
details of the selected metadata, the violation form describes for Ph.D. students and 1 Ph.D., all of which were familiar with the
each LHS value combination involved in a violation: 𝑖) the value given domain. Statistics about participants have been collected
combination of the LHS, 𝑖𝑖) the corresponding distinct values through a background survey, as shown in Table 1(Q1-Q4), and
found on the RHS, and 𝑖𝑖𝑖) their total number (see Figure 5(a)). whose results are reported in Figure 6. In particular, about 85%
Instead, concerning uccs, apart from the details of the selected of the recruited people were men, 15% were women, and most of
metadata, the violation form describes for each value combination them were undergraduate students. Moreover, on average they
involved in value duplication: 𝑖) the value combination involved declared, through a Likert scale, a medium level of knowledge
in a duplication, 𝑖𝑖) the number of duplications (see Figure 5(b)). concerning MySQL and MySQL Workbench. Before the evalu-
The latter should represent the functionality that drives users in ation started, participants underwent a 45-min tutorial on the
accomplishing the best possible correction of errors. theoretical foundations of data profiling and inditio.
Each of the 90 participants was given a database concerning
Example 4.3. Figure 5(a) shows the violation details of the personal data, three data insertion statements, and two sets of
fd C → E (e.g. ram → display_ppi) according to the validation uccs and fds metadata, respectively. Moreover, we requested
(a) A form showing validation statistics after the fd validation process. (b) A form showing validation statistics after the ucc validation process.
Figure 4: Validation statistics provided by inditio.
(a) A form showing values that invalidate metadata after the fd validation process. (b) A form showing values that invalidate metadata after the ucc validation process.
Figure 5: Violation details of inditio.
them to check the correctness of data insertion statements ac- validate the metadata by directly analyzing the data source or
cording to the provided metadata, and if necessary, to correct using the SQL language to compose specific queries. To this end,
statements aiming to guarantee the validity of the provided meta- they could interact only with the tools already integrated into
data. More specifically, we conducted a within subjects study MySQL Workbench, both to validate the metadata and to correct
by considering two scenarios: with and without inditio, and the values within the data.
requested to accomplish the task in one scenario first, and then After completing the assigned tasks, participants were re-
with the other one. Half participants considered first the scenario quested to fill some questionnaires, aiming to highlight the ad-
without inditio, while the remaining ones used inditio first. vantages and drawbacks of inditio (see Table 1(Q4-Q20)). More
Notice that, the provided data insertion statements were different specifically, questions from Q4 to Q8 have been filled after par-
but equivalent in complexity. In particular, in the case partici- ticipants performed each task (with and without inditio, or vice
pants performed the tasks without the tool, they were able to versa), whereas the remaining ones have been included in a final
survey. Moreover, questions from Q4 to Q20 are quantitative, and
they have been measured through a Likert scale, ranging from 1,
High School Diploma Bachelor Degree Master Degree Ph.D. mapping “Strongly disagree” response, to 5, mapping “Strongly
Q2 81 5 4 1 agree” response. Finally, to further evaluate the effectiveness of
both processes (with and without inditio) we measured the time
required for completing the task and the number of errors.
1 = Sufficient 2 3 4 5 = Very Good
Q3 5 8 49 23 5
5.2 Results and Discussion
Q4 5 16 47 19 3 Figure 8 shows the results achieved from each participant while
executing the assigned tasks with both the compared scenarios
(without and with the tool). The results achieved from the tasks
performed without the tool have been considered as the compar-
Figure 6: Statistics concerning involved participants. ative baseline (purple line), while the results obtained with the
Survey Alias Question
Gender
Background
Q1
Q2 Qualification
Q3 Level of knowledge of MySQL
Q4 Level of knowledge of MySQL Workbench
I completed the tasks quickly and easily
Comparative
Q5
Q6 The instructions for completing the tasks are clear and easy to read
Q7 The metadata validation process has been simple
Q8 The values that invalidated the metadata have been easy to find
Q9 The tool is simple to use
Q10 The tool is simple to learn
Q11 I was able to retrieve back the process, whenever I made a mistake
Q12 The tool shows the information very clearly
Q13 The tool is pleasant to use
Q14 The user interface is pleasant and informative
Q15 The tool made transparent the execution of the underlying validation processes
It was simple to understand the metadata syntax
Final
Q16
Q17 The tool presents all the features I expected
Q18 I am in general satisfied about the tool
Q19 In the future, I would like to use the tool
Q20 The tool simplified the validation process with respect to the manual process
Q21 What is your general impression about the tool?
Q22 Do you have any improvements to suggest?
Q23 Which feature of the tool did you like the least?
Q24 Which feature of the tool did you like the most?
Table 1: Questions proposed to participants.
proposed tool were described by the bars. In particular, the plot simple to learn, and pleasant to use (see Figure 9: answers for Q10
highlights the improvement obtained by using inditio, e.g., a and Q13). Nevertheless, some work should be made to further
value of 2 indicates that the results achieved with inditio are 2 improve the general usability of the plugin and the transparency
times better than those achieved without it, whereas a value less of the validation process, according to answers to questions to
than 1 indicates the opposite case. In general, it can be observed Q11, Q12, and Q15.
that most of the participants performed better with inditio, even The open questions in the final questionnaire (see Q21-Q24
if satisfactory results have been achieved also without the use in Table 1) aimed at highlighting the strengths and weakness
of the plugin. Concerning the time employed to complete the of inditio. In particular, concerning the general impressions
assigned tasks, on average participants took thirty minutes with about the plugin (see Q21 in Table 1), many participants said that
inditio, ranging from five to sixty-seven minutes, and forty-five inditio shows a simple and intuitive interface, in which the com-
minutes without it, ranging from five to eighty-four minutes. In ponents appear well organized in the frame. Another part of them
general, we noticed that the manual validation task times were expressed their opinion on the usefulness and efficiency of the
particularly long, especially for users with less knowledge of
MySQL. On the contrary, with inditio almost all users have
reduced the time of the validation processes by more than 50%. Without INDITIO With INDITIO
Figure 7 depicts the box plots derived from the answers (on a 5.0
Likert scale from 1 to 5) to questions ranging from Q5 to Q8. In
4.5
particular, users answered the same questions after performing
tasks for each considered scenario (with and without the tool). 4.0
A boxplot shows the median (horizontal lines), the interquar-
3.5
tile ranges (boxes), the largest and the smallest observations
(whiskers). By comparing the results achieved without the plugin 3.0
(the first four box plots in Figure 7) against those with the plugin,
2.5
(the remaining box plots) we can conclude that participants felt
more comfortable and effective when working with inditio. 2.0
Concerning the quantitative questions in the final question-
1.5
naire, inditio obtained the general agreement of participants,
while evaluating its usability and effectiveness (see Figure 9). 1.0
In particular, according to answers for Q20, the capability of Q5 Q6 Q7 Q8 Q5 Q6 Q7 Q8
simplifying the metadata validation process has been widely rec-
ognized to inditio. The latter has turned out to be comfortable Figure 7: Comparative boxplots showing distribution of
and useful according to answers to questions to Q18 and Q19, user answers to the quantitative questionnaire.
2.50
With INDITIO Without INDITIO
2.25
2.00
1.75
Improvement
1.50
1.25
1.00
0.75
0.50
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Figure 8: Distributing scores achieved by participants for each analyzed scenario (with and without inditio).
plugin, claiming that “The tool has certainly been successful in its Finally, we have asked users some suggestions for enhanc-
intent, significantly speeding up the time for validating metadata”. ing inditio (see Q24 in Table 1). To this end, some users have
Moreover, some of the most interesting comments have been pro- suggested integrating new metadata, also allowing them to simul-
vided from users that are less familiar with the research context taneously validate multiple metadata. Other users have suggested
and with MySQL Workbench. In fact, they report that inditio improving the integration with systems based on the Linux ar-
is able to improve the understanding of MySQL Workbench and chitecture. In fact, it has been found that some of the users using
to show that metadata potentially allows them to extract further these operating systems tend to view some reports differently
knowledge from data, which often is not clearly visible. Only a from users who use Windows systems. However, this is due to
small part of inexperienced users asserted that the plugin inter- the compatibility problems between the technologies underly-
face might be initially unclear, claiming that “Initially, the tool ing the MySQL Workbench and different operating systems. In
seems difficult to understand. Then, once I understand how to use it, the future, these compatibility issues might be solved with new
it is very useful for checking the validity of fd and ucc”. However, software versions.
by practising with it, inditio allowed them to easily understand In summary, the four open questions of the final questionnaire
its features and to become familiar with its environment. revealed that some participants remarked some limitations of
In addition, concerning the specific features that participants the inditio user interface. Moreover, they would like to receive
liked the most and the least (see Q22-Q23 in Table 1), experienced more hints during the statement modification process, accord-
users have greatly appreciated the functionality of identifying ing to validation results. Conversely, they positively judged the
values yielding the invalidation of metadata. In fact, some of them intuitiveness the metadata validation and the error detection
claimed that “One of the most interesting features is identifying processes. Moreover, they welcomed the tool and recognized its
the values that invalidate the metadata. This functionality could usefulness.
be directly integrated into the MySql Workbench suite.” Instead,
inexperienced users have shown interest for the report forms (see
Figure 5) and for the simplicity through which the plugin could be 6 CONCLUSION
integrated within the MySQL Workbench suite as a simple plugin. In this paper we presented inditio, a MySQL plugin enabling
Although most of the comments were positive, we also investi- users to assess the quality of data they planned to insert into a
gated the features they liked the least. Among them, many users database. In particular, the plugin enables a user to observe how
have highlighted that the Query Editor component appears small the new tuples can affect the validation of some metadata, e.g.
and does not clearly show statements. However, this is limited fds and uccs, assisting him/her in correcting them, if needed. We
by the graphics components included in the MySQL Workbench. evaluated inditio by involving more than eighty participants in
For these reasons, we allow the users to directly interact with a user study, which demonstrated its usefulness, giving us the
the SQL editor of MySQL Workbench, and to import their state- possibility to detect the main characteristics of the tool to be
ments. Other participants have suggested adding further reports improved, mainly focusing on the improvement and/or extension
in the interface in order to enhance their understanding of how of some visual components.
data insertion statements affect metadata. Only few users have The potentiality of inditio yields many possible future direc-
proposed to extend the interface of inditio with new graphical tions. In particular, we are currently working on the enrichment
components in order to improve the interaction with both the of the set of possible metadata that can be taken into consid-
plugin and the MySQL Workbench. eration, with particularly emphasis on relaxed fds (rfds) [8].
1 = Strongly disagree 2 3 4 5 = Strongly agree
Q9 1 1 18 36 34
Q10 1 14 28 47
Q11 2 13 25 33 17
Q12 2 7 11 40 30
Q13 2 2 14 39 33
Q14 1 7 30 33 19
Q15 3 7 27 30 23
Q16 1 5 22 41 21
Q17 8 18 37 27
Q18 1 11 49 29
Q19 1 3 5 37 44
Q20 1 5 21 63
Figure 9: Distributing participant answers to the quantitative questions in the final questionnaire.
Moreover, we would like to directly connect the plugin to in- Applications 78, 23 (2019), 33151–33167.
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