Please refer to the content listed below –> “Data Wrangling – Big -data . Do you agree with the conclusion in the article that says “Data wrangling is a problem and an opportunity”? Please present your analysis. 
 

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Data Wrangling – Big data  

Data Wrangling for Big Data: Challenges and
Opportunities

Tim Furche
Dept. of Computer Science

Oxford University
Oxford OX1 3QD, UK

tim.furche@cs.ox.ac.uk

Georg Gottlob
Dept. of Computer Science

Oxford University
Oxford OX1 3QD, UK

georg.gottlob@cs.ox.ac.uk

Leonid Libkin
School of Informatics

University of Edinburgh
Edinburgh EH8 9AB, UK

libkin@ed.ac.uk
Giorgio Orsi

School. of Computer Science
University of Birmingham
Birmingham, B15 2TT, UK

G.Orsi@cs.bham.ac.uk

Norman W. Paton
School of Computer Science

University of Manchester
Manchester M13 9PL, UK

npaton@manchester.ac.uk

ABSTRACT
Data wrangling is the process by which the data required by an ap-
plication is identified, extracted, cleaned and integrated, to yield a
data set that is suitable for exploration and analysis. Although there
are widely used Extract, Transform and Load (ETL) techniques and
platforms, they often require manual work from technical and do-
main experts at different stages of the process. When confronted
with the 4 V’s of big data (volume, velocity, variety and veracity),
manual intervention may make ETL prohibitively expensive. This
paper argues that providing cost-effective, highly-automated ap-
proaches to data wrangling involves significant research challenges,
requiring fundamental changes to established areas such as data ex-
traction, integration and cleaning, and to the ways in which these
areas are brought together. Specifically, the paper discusses the im-
portance of comprehensive support for context awareness within
data wrangling, and the need for adaptive, pay-as-you-go solutions
that automatically tune the wrangling process to the requirements
and resources of the specific application.

1. INTRODUCTION
Data wrangling has been recognised as a recurring feature of big

data life cycles. Data wrangling has been defined as:

a process of iterative data exploration and transforma-
tion that enables analysis. ([21])

In some cases, definitions capture the assumption that there is sig-
nificant manual effort in the process:

the process of manually converting or mapping data
from one “raw” form into another format that allows
for more convenient consumption of the data with the
help of semi-automated tools. ([35])

c©2016, Copyright is with the authors. Published in Proc. 19th Inter-
national Conference on Extending Database Technology (EDBT), March
15-18, 2016 – Bordeaux, France: ISBN 978-3-89318-070-7, on OpenPro-
ceedings.org. Distribution of this paper is permitted under the terms of the
Creative Commons license CC-by-nc-nd 4.0

The general requirement to reorganise data for analysis is noth-
ing new, with both database vendors and data integration compa-
nies providing Extract, Transform and Load (ETL) products [34].
ETL platforms typically provide components for wrapping data
sources, transforming and combing data from different sources,
and for loading the resulting data into data warehouses, along with
some means of orchestrating the components, such as a workflow
language. Such platforms are clearly useful, but in being developed
principally for enterprise settings, they tend to limit their scope to
supporting the specification of wrangling workflows by expert de-
velopers.

Does big data make a difference to what is needed for ETL? Al-
though there are many different flavors of big data applications,
the 4 V’s of big data1 refer to some recurring characteristics: Vol-
ume represents scale either in terms of the size or number of data
sources; Velocity represents either data arrival rates or the rate
at which sources or their contents may change; Variety captures
the diversity of sources of data, including sensors, databases, files
and the deep web; and Veracity represents the uncertainty that is
inevitable in such a complex environment. When all 4 V’s are
present, the use of ETL processes involving manual intervention
at some stage may lead to the sacrifice of one or more of the V’s to
comply with resource and budget constraints. Currently,

data scientists spend from 50 percent to 80 percent of
their time collecting and preparing unruly digital data.
([24])

and only a fraction of an expert’s time may be dedicated to value-
added exploration and analysis.

In addition to the technical case for research in data wrangling,
there is also a significant business case; for example, vendor rev-
enue from big data hardware, software and services was valued at
$13B in 2013, with an annual growth rate of 60%. However, just as
significant is the nature of the associated activities. The UK Gov-
ernment’s Information Economy Strategy states:

the overwhelming majority of information economy
businesses – 95% of the 120,000 enterprises in the sec-
tor – employ fewer than 10 people. ([14])

As such, many of the organisations that stand to benefit from big
data will not be able to devote substantial resources to value-added
1http://www.ibmbigdatahub.com/infographic/
four-vs-big-data.

http://www.ibmbigdatahub.com/infographic/four-vs-big-data

http://www.ibmbigdatahub.com/infographic/four-vs-big-data

data analyses unless massive automation of wrangling processes is
achieved, e.g., by limiting manual intervention to high-level feed-
back and to the specification of exceptions.

Example 1 (e-Commerce Price Intelligence). When running an e-
Commerce site, it is necessary to understand pricing trends among
competitors. This may involve getting to grips with: Volume –
thousands of sites; Velocity – sites, site descriptions and contents
that are continually changing; Variety – in format, content, targeted
community, etc; and Veracity – unavailability, inconsistent descrip-
tions, unavailable offers, etc. Manual data wrangling is likely to be
expensive, partial, unreliable and poorly targeted.

As a result, there is a need for research into how to make data
wrangling more cost effective. The contribution of this vision pa-
per is to characterise research challenges emerging from data wran-
gling for the 4Vs (Section 2), to identify what existing work seems
to be relevant and where it needs to be further developed (Sec-
tion 3), and to provide a vision for a new research direction that
is a prerequisite for widespread cost-effective exploitation of big
data (Section 4).

2. DATA WRANGLING – RESEARCH
CHALLENGES

As discussed in the introduction, there is a need for cost-effective
data wrangling; the 4 V’s of big data are likely to lead to the man-
ual production of a comprehensive data wrangling process being
prohibitively expensive for many users. In practice this means that
data wrangling for big data involves: (i) making compromises –
as the perfect solution is not likely to be achievable, it is neces-
sary to understand and capture the priorities of the users and to use
these to target resources in a cost-effective manner; (ii) extending
boundaries – as relevant data may be spread across many organ-
isations and of many types; (iii) making use of all the available
information – applications differ not only in the nature of the rele-
vant data sources, but also in existing resources that could inform
the wrangling process, and full use needs to be made of existing ev-
idence; and (iv) adopting an incremental, pay-as-you-go approach
– users need to be able to contribute effort to the wrangling process
in whatever form they choose and at whatever moment they choose.

The remainder of this section expands on these features, pointing
out the challenges that they present to researchers.

2.1 Making Compromises
Faced with an application exhibiting the 4 V’s of big data, data

scientists may feel overwhelmed by the scale and difficulty of the
wrangling task. It will often be impossible to produce a compre-
hensive solution, so one challenge is to make well informed com-
promises.

The user context of an application specifies functional and non-
functional requirements of the users, and the trade-offs between
them.

Example 2 (e-Commerce User Contexts). In price intelligence,
following on from Example 1, there may be different user contexts.
For example, routine price comparison may be able to work with
a subset of high quality sources, and thus the user may prefer fea-
tures such as accuracy and timeliness to completeness. In contrast,
where sales of a popular item have been falling, the associated issue
investigation may require a more complete picture for the product
in question, at the risk of presenting the user with more incorrect or
out-of-date data.

Thus a single application may have different user contexts, and
any approach to data wrangling that hard-wires a process for se-

lecting and integrating data risks the production of data sets that
are not always fit for purpose. Making well informed compro-
mises involves: (i) capturing and making explicit the requirements
and priorities of users; and (ii) enabling these requirements to per-
meate the wrangling process. There has been significant work on
decision-support, for example in relation to multi-criteria decision
making [37], that provides both languages for capturing require-
ments and algorithms for exploring the space of possible solutions
in ways that take the requirements into account. For example, in
the widely used Analytic Hierarchy Process [31], users compare
criteria (such as timeliness or completeness) in terms of their rel-
ative importance, which can be taken into account when making
decisions (such as which mappings to use in data integration).

Although data management researchers have investigated tech-
niques that apply specific user criteria to inform decisions (e.g. for
selecting sources based on their anticipated financial value [16])
and have sometimes traded off alternative objectives (e.g. precision
and recall for mapping selection and refinement [5]), such results
have tended to address specific steps within wrangling in isolation,
often leading to bespoke solutions. Together with high automation,
adaptivity and multi-criteria optimisation are of paramount impor-
tance for cost-effective wrangling processes.

2.2 Extending the Boundaries
ETL processes traditionally operate on data lying within the

boundaries of an organisation or across a network of partners. As
soon as companies started to leverage big data and data science, it
became clear that data outside the boundaries of the organisation
represent both new business opportunities as well as a means to
optimize existing business processes.

Data wrangling solutions recently started to offer connectors to
external data sources but, for now, mostly limited to open govern-
ment data and established social networks (e.g., Twitter) via for-
malised APIs. This makes wrangling processes dependent on the
availability of APIs from third parties, thus limiting the availability
of data and the scope of the wrangling processes.

Recent advances in web data extraction [19, 30] have shown that
fully-automated, large scale collection of long-tail, business-related
data, e.g., products, jobs or locations, is possible. The challenge for
data wrangling processes is now to make proper use of this wealth
of “wild” data by coordinating extraction, integration and cleaning
processes.

Example 3 (Business Locations). Many social networks offer the
ability for users to check-in to places, e.g., restaurants, offices, cine-
mas, via their mobile apps. This gives to social networks the ability
to maintain a database of businesses, their locations, and profiles of
users interacting with them that is immensely valuable for advertis-
ing purposes. On the other hand, this way of acquiring data is prone
to data quality problems, e.g., wrong geo-locations, misspelled or
fantasy places. A popular way to address these problems is to ac-
quire a curated database of geo-located business locations. This is
usually expensive and does not always guarantee that the data is
really clean, as its quality depends on the quality of the (usually
unknown) data acquisition and curation process. Another way is
to define a wrangling process that collects this information right on
the website of the business of interest, e.g., by wrapping the tar-
get data source directly. The extraction process can in this case be
“informed” by existing integrated data, e.g., the business url and
a database of already known addresses, to identify previously un-
known locations and correct erroneous ones.

2.3 Using All the Available Information
Cost-effective data wrangling will need to make extensive use of

automation for the different steps in the wrangling process. Auto-
mated processes must take advantage of all available information
both when generating proposals and for comparing alternative pro-
posals in the light of the user context.

The data context of an application consists of the sources that
may provide data for wrangling, and other information that may
inform the wrangling process.

Example 4 (e-Commerce Data Context). In price intelligence, fol-
lowing on from Example 1, the data context includes the catalogs
of the many online retailers that sell overlapping sets of products to
overlapping markets. However, there are additional data resources
that can inform the process. For example, the e-Commerce com-
pany has a product catalog that can be considered as master data by
the wrangling process; the company is interested in price compari-
son only for the products it sells. In addition, for this domain there
are standard formats, for example in schema.org, for describing
products and offers, and there are ontologies that describe products,
such as The Product Types Ontology2.

Thus applications have different data contexts, which include not
only the data that the application seeks to use, but also local and
third party sources that provide additional information about the
domain or the data therein. To be cost-effective, automated tech-
niques must be able to bring together all the available information.
For example, a product types ontology could be used to inform the
selection of sources based on their relevance, as an input to the
matching of sources that supplements syntactic matching, and as a
guide to the fusion of property values from records that have been
obtained from different sources. To do this, automated processes
must make well founded decisions, integrating evidence of differ-
ent types. In data management, there are results of relevance to data
wrangling that assimilate evidence to reach decisions (e.g. [36]),
but work to date tends to be focused on small numbers of types
of evidence, and individual data management tasks. Cost effective
data wrangling requires more pervasive approaches.

2.4 Adopting a Pay-as-you-go Approach
As discussed in Section 1, potential users of big data will not

always have access to substantial budgets or teams of skilled data
scientists to support manual data wrangling. As such, rather than
depending upon a continuous labor-intensive wrangling effort, to
enable resources to be deployed on data wrangling in a targeted and
flexible way, we propose an incremental, pay-as-you-go approach,
in which the “payment” can take different forms.

Providing a pay-as-you-go approach, with flexible kinds of pay-
ment, means automating all steps in the wrangling process, and al-
lowing feedback in whatever form the user chooses. This requires
a flexible architecture in which feedback is combined with other
sources of evidence (see Section 2.3) to enable the best possible de-
cisions to be made. Feedback of one type should be able to inform
many different steps in the wrangling process – for example, the
identification of several correct (or incorrect) results may inform
both source selection and mapping generation. Although there has
been significant work on incremental, pay-as-you-go approaches to
data management, building on the dataspaces vision [18], typically
this has used one or a few types of feedback to inform a single ac-
tivity. As such, there is significant work to be done to provide a
more integrated approach in which feedback can inform all steps
of the wrangling process.

Example 5 (e-Commerce Pay-as-you-go). In Example 1, auto-
mated approaches to data wrangling can be used to select sources of
2http://www.productontology.org/

product data, and to fuse the values from such sources to provide re-
ports on the pricing of different products. These reports are studied
by the data scientists of the e-Commerce company who are review-
ing the pricing of competitors, who can annotate the data values in
the report, for example, to identify which are correct or incorrect,
along with their relevance to decision-making. Such feedback can
trigger the data wrangling system to revise the way in which such
reports are produced, for example by prioritising results from dif-
ferent data sources. The provision of domain-expert feedback from
the data scientists is a form of payment, as staff effort is required to
provide it. However, it should also be possible to use crowdsourc-
ing, with direct financial payment of crowd workers, for example
to identify duplicates, and thereby to refine the automatically gen-
erated rules that determine when two records represent the same
real-world object [20]. It is of paramount importance that these
feedback-induced “reactions” do not trigger a re-processing of all
datasets involved in the computation but rather limit the processing
to the strictly necessary data.

3. DATA WRANGLING – RELATED
WORK

As discussed in Section 2, cost-effective data wrangling is ex-
pected to involve best-effort approaches, in which multiple sources
of evidence are combined by automated techniques, the results of
which can be refined following a pay-as-you-go approach. Space
precludes a comprehensive review of potentially relevant results,
so in this section we focus on three areas with overlapping require-
ments and approaches, pointing out existing results on which data
wrangling can build, but also areas in which these results need to
be extended.

3.1 Knowledge Base Construction
In knowledge base construction (KBC) the objective is to au-

tomatically create structured representations of data, typically us-
ing the web as a source of facts for inclusion in the knowledge
base. Prominent examples include YAGO [33], Elementary [28]
and Google’s Knowledge Vault [15], all of which combine candi-
date facts from web data sources to create or extend descriptions of
entities. Such proposals are relevant to data wrangling, in providing
large scale, automatically generated representations of structured
data extracted from diverse sources, taking account of the associ-
ated uncertainties.

These techniques have produced impressive results but they tend
to have a single, implicit user context, with a focus on consolidating
slowly-changing, common sense knowledge that leans heavily on
the assumption that correct facts occur frequently (instance-based
redundancy). For data wrangling, the need to support diverse user
contexts and highly transient information (e.g., pricing) means that
user requirements need to be made explicit and to inform decision-
making throughout automated processes. In addition, the focus
on fully automated KBC at web-scale, without systematic support
for incremental improvement in a pay-as-you-go manner, tends
to require expert input, for example through the writing of rules
(e.g., [28]). As such, KBC proposals share requirements with data
wrangling, but have different emphases.

3.2 Pay-as-you-go Data Management
Pay-as-you-go data management, as represented by the datas-

paces vision [18], involves the combination of an automated boot-
strapping phase, followed by incremental improvement. There have
been numerous results on different aspects of pay-as-you-go data
management, across several activities of relevance to data wran-

schema.org

http://www.productontology.org/

gling, such as data extraction (e.g., [12]), matching [26], map-
ping [5] and entity resolution [20]. We note that in these proposals a
single type of feedback is used to support a single data management
task. The opportunities presented by crowdsourcing have provided
a recent boost to this area, in which, typically, paid micro-tasks are
submitted to public crowds as a source of feedback for pay-as-you-
go activities. This has included work that refines different steps
within an activity (e.g. both blocking and fine-grained matching
within entity resolution [20]), and the investigation of systematic
approaches for relating uncertain feedback to other sources of ev-
idence (e.g., [13]). However, the state-of-the-art is that techniques
have been developed in which individual types of feedback are used
to influence specific data management tasks, and there seems to be
significant scope for feedback to be integrated into all activities
that compose a data wrangling pipeline, with reuse of feedback to
inform multiple activities [6]. Highly automated wrangling pro-
cesses require formalised feedback (e.g., in terms of rules or facts
to be added/removed from the process) so that they can be used by
suitable reasoning processes to automatically adapt the wrangling
workflows.

Data Tamer [32] provides a substantially automated pipeline in-
volving schema integration and entity resolution, where compo-
nents obtain feedback to refine the results of automated analy-
ses. Although Data Tamer moves a significant way from classi-
cal, largely manually specified ETL techniques, user feedback is
obtained for and applied to specific steps (and not shared across
components), and there is no user context to inform where compro-
mises should be made and efforts focused.

3.3 Context Awareness
There has been significant prior work on context in comput-

ing systems [3], with a particular emphasis on mobile devices and
users, in which the objective is to provide data [9] or services [25]
that meet the evolving, situational needs of users. In information
management, the emphasis has been on identifying the portion of
the available information that is relevant in specific ambient condi-
tions [8]. For data wrangling, classical notions of context such as
location and time will sometimes be relevant, but we anticipate that
for data wrangling: (i) there may be many additional features that
characterise the user and data contexts, for individual users, groups
of users and tasks; and (ii) that the information about context will
need to inform a wide range of data management tasks in addition
to the selection of the most relevant results.

4. DATA WRANGLING – VISION
In the light of the scene-setting from the previous sections, Fig-

ure 1 outlines potential components and relationships in a data
wrangling architecture. To the left of the figure, several (poten-
tially many) Data Sources provide the data that is required for the
application. A Data Extraction component provides wrappers for
the potentially heterogeneous sources (files, databases, documents,
web pages), providing syntactically consistent representations that
can then be brought together by the Data Integration component,
to yield Wrangled Data that is then available for exploration and
analysis.

However, in our vision, these extraction and integration compo-
nents both use all the available data and adopt a pay-as-you-go
approach. In Figure 1, this is represented by a collection of Work-
ing Data, which contains not only results and metadata from the
Data Extraction and Data Integration components, but also:

1. all relevant Auxiliary Data, which would include the user
context, and whatever additional information can represent
the data context, such as reference data, master data and do-

Figure 1: Abstract Wrangling Architecture.

main ontologies;
2. the results of all Quality analyses that have been carried out,

which may apply to individual data sources, the results of
different extractions and components of relevance to integra-
tion such as matches or mappings; and

3. the feedback that has been obtained from users or crowds,
on any aspect of the wrangling process, including the ex-
tractions (e.g. users could indicate if a wrapper has extracted
what they would have expected), or the results of integrations
(e.g. crowds could identify duplicates).

To support this, data wrangling needs substantial advances in
data extraction, integration and cleaning, as well as the co-design
of the components in Figure 1 to support a much closer interaction
in a context-aware, pay-as-you-go setting.

4.1 Research Challenges for Components
This section makes the case that meeting the vision will require

changes of substance to existing data management functionalities,
such as Data Extraction and Data Integration.

To respond fully to the proposed architecture, Data Extraction
must make effective use of all the available data. Consider web
data extraction, in which wrappers are generated that enable deep
web resources to be treated as structured data sets (e.g., [12, 19]).
The lack of context and incrementality in data extraction has long
been identified as a weakness [11], and research is required to make
extraction components responsive to quality analyses, insights from
integration and user feedback. As an example, existing knowledge
bases and intermediate products of data cleaning and integration
processes can be used to improve the quality of wrapper induction
(e.g. [29]).

Along the same lines, Data Integration must make effective use
of all the available data in ways that take account of the user con-
text. As data integration acts on a variety of constructs (sources,
matches, mappings, instances), each of which may be associated
with its own uncertainties, automated functionalities such as those
for identifying matches and generating mappings need to be re-
vised to support multi-criteria decision making in the context of
uncertainty. For example, the selection of which mappings to use
must take into account information from the user context, such as
the number of results required, the budget for accessing sources,
and quality requirements. To support the complete data wrangling

process involves generalising from a range of point solutions into
an approach in which all components can take account of a range
of different sources of evolving evidence.

4.2 Research Challenges for Architectures
This section makes the case that meeting the vision will require

changes of substance to existing data management architectures,
and in particular a paradigm-shift for ETL.

Traditional ETL operates on manually-specified data manipula-
tion workflows that extract data from structured data sources, inte-
grating, cleaning, and eventually storing them in aggregated form
into data warehouses. In Figure 1 there is no explicit control flow
specified, but we note that the requirements of automation, refined
on a pay-as-you-go basis taking into account the user context, is
at odds with a hard-wired, user-specified data manipulation work-
flow. In the abstract architecture, the pay-as-you-go approach is
achieved by storing intermediate results of the ETL process for
on-demand recombination, depending on the user context and the
potentially continually evolving data context. As such, the user
context must provide a declarative specification of the user’s re-
quirements and priorities, both functional (data) and non-functional
(such as quality and cost trade-offs), so that the components in Fig-
ure 1 can be automatically and flexibly composed. Such an ap-
proach requires an autonomic approach to data wrangling, in which
self-configuration is more central to the architecture than in self-
managing databases [10].

The resulting architecture must not only be autonomic, it must
also take account of the inherent uncertainty associated with much
of the Working Data in Figure 1. Uncertainty comes from: (i)
Data Sources in the form of unreliable and inconsistent data; (ii)
the wrangling components, for example in the form of tentative ex-
traction rules or mappings; (iii) the auxiliary data, for example in
the form of ontologies that do not quite represent the user’s con-
ceptualisation of the domain; and (iv) the feedback which may
be unreliable or out of line with the user’s requirements or pref-
erences. With this complex environment, it is important that uncer-
tainty is represented explicitly and reasoned with systematically, so
that well informed decisions can build on a sound understanding of
the available evidence.

This raises an additional research question, on how best to repre-
sent and reason in a principled and scalable way with the working
data and associated workflows; there is a need for a uniform rep-
resentation for the results of the different components in Figure 1,
which are as diverse as domain ontologies, matches, data extrac-
tion and transformation rules, schema mappings, user feedback and
provenance information, along with their associated quality anno-
tations and uncertainties.

In addition, the ways in which different types of user engage with
the wrangling process is also worthy of further research. In Wran-
gler [22], now commercialised by Trifacta, data scientists clean and
transform data sets using an interactive interface in which, among
other things, the system can suggest generic transformations from
user edits. In this approach, users provide feedback on the changes
to the selected data they would like to have made, and select from
proposed transformations. Additional research could investigate
where such interactions could be used to inform upstream aspects
of the wrangling process, such as source selection or mapping gen-
eration, and to understand how other kinds of feedback, or the re-
sults of other analyses, could inform what is offered to the user in
tools such as Wrangler.

4.3 Research Challenges in Scalability
In this paper we have proposed responding to the Volume as-

pect of big data principally in the form of the number of sources
that may be available, where we propose that automation and in-
crementality are key approaches. In this section we discuss some
additional challenges in data wrangling that result from scale.

The most direct impact of scale in big data results from the sheer
volume of data that may be present in the sources. ETL vendors
have responded to this challenge by compiling ETL workflows into
big data platforms, such as map/reduce. In the architecture of Fig-
ure 1, it will be necessary for extraction, integration and data query-
ing tasks to be able to be executed using such platforms. However,
there are also fundamental problems to be addressed. For example,
many quality analyses are intractable (e.g. [7]), and evaluating even
standard queries of the sort used in mappings may require substan-
tial changes to classical assumptions when faced with huge data
sets. Among these challenges are understanding the requirement
for query scalability [2] that can be provided in terms of access
and indexing information [17], and developing static techniques for
query approximation (i.e., without looking at the data) as was initi-
ated in [4] for conjunctive queries. For the architecture of Figure 1
there is the additional requirement to reason with uncertainty over
potentially numerous sources of evidence; this is a serious issue
since even in the classical settings data uncertainty often leads to
intractability of the most basic data processing tasks [1, 23]. We
also observe that knowledge base construction has itself given rise
to novel reasoning techniques [27], and additional research may be
required to inform decision-making for data wrangling at scale.

5. CONCLUSIONS
Data wrangling is a problem and an opportunity:
• A problem because the 4 V’s of big data may all be present

together, undermining manual approaches to ETL.
• An opportunity because if we can make data wrangling much

more cost effective, all sorts of hitherto impractical tasks
come into reach.

This vision paper aims to raise the profile of data wrangling as a
research area within the data management community, where there
is a lot of work on relevant functionalities, but where these have not
been refined or integrated as is required to support data wrangling.
The paper has identified research challenges that emerge from data
wrangling, around the need to make compromises that reflect the
user’s requirements, the ability to make use of all the available ev-
idence, and the development of pay-as-you-go techniques that en-
able diverse forms of payment at convenient times. We have also
presented an abstract architecture for data wrangling, and outlined
how that architecture departs from traditional approaches to ETL,
through increased use of automation, which flexibly accounts for
diverse user and data contexts. It has been suggested that this archi-
tecture will require changes of substance to established data man-
agement components, as well as the way in which they work to-
gether. For example, the proposed architecture will require support
for representing and reasoning with the diverse and uncertain work-
ing data that is of relevance to the data wrangling process. Thus we
encourage the data management research community to direct its
attention at novel approaches to data wrangling, as a prerequisite
for the cost-effective exploitation of big data.

Acknowledgments
This research is supported by the VADA Programme Grant from the
UK Engineering and Physical Sciences Research Council, whose
support we are pleased to acknowledge. We are also grateful to our
colleagues in VADA for their contributions to discussions on data
wrangling: Peter Buneman, Wenfei Fan, Alvaro Fernandes, John

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http://bit.ly/1W4TPGU

http://bit.ly/1KslZb7

• Introduction
• Data Wrangling – Research Challenges

Making Compromises
Extending the Boundaries
Using All the Available Information
Adopting a Pay-as-you-go Approach

• Data Wrangling – Related Work

Knowledge Base Construction
Pay-as-you-go Data Management
Context Awareness

• Data Wrangling – Vision

Research Challenges for Components
Research Challenges for Architectures
Research Challenges in Scalability

• Conclusions
• References