Exploiting Linked Data Towards the Production of Added-Value

Business Analytics and Vice-versa

Eleni Fotopoulou

, Panagiotis Hasapis

, Anastasios Zafeiropoulos

, Dimitris Papaspyros

Spiros Mouzakitis

and Norma Zanetti

Ubitech Ltd., Thessalias 8 & Etolias 10, Chalandri, Athens, Greece

School of Electrical and Computer Engineering, National Technical University of Athens,

Heroon Polytechneiou 9, Zografou, Athens, Greece

Hyperborea Srl., Via Giuntini 25, Navacchio (Pi), Italy

Keywords: Linked Data, Open Data, Business Analytics, Data Mining, Ontology.

Abstract: The majority of enterprises are in the process of recognizing that business data analytics have the potential

to transform their daily operations and make them extremely effective at addressing business challenges,

identifying new market trends and embracing new ways to engage customers. Such analytics are in most

cases related with the processing of data coming from various data sources that include structured and

unstructured data. In order to get insight through the analysis results, appropriate input has to be provided

that in many cases has to combine data from cross-sectorial and heterogeneous public or private data

sources. Thus, there is inherent a need for applying novel techniques in order to harvest complex and

heterogeneous datasets, turn them into insights and make decisions. In this paper, we present an approach

for the production of added-value business analytics through the consumption of interlinked versions of data

and the exploitation of linked data principles. Such interlinked data constitute valuable input for the

initiation of an analytics extraction process and can lead to the realization of analysis that was not envisaged

in the past. In addition to the production of analytics based on the consumption of linked data, the proposed

approach supports the interlinking of the produced results with the associated input data, increasing in this

way the value of the produced data and making them discoverable for further use in the future. The designed

business analytics and data mining component is described in detail, along with an indicative application

scenario combining data from the governmental, societal and health sectors.

1 INTRODUCTION

During the last years, there is evident an increasing

trend in the produced volumes of business data, both

structured and unstructured. This trend is evolving,

since, as stated in a study from eBay (eBay report,

2013), the volume of business data worldwide across

all companies is estimated to double every 1.2 years.

This continuous increase in the produced data across

various business sectors is also referred as

“datafication” of the world (Kreissl, 2013).

However, “datafication” cannot be automatically

associated with new insights and advances in our

understanding of business data and the production of

knowledge (Lausch, 2015). In order to gain insight,

available data has to be appropriately represented -in

many cases upon transformation and harmonization

processes- and be processed both statistically and

analytically (Lausch, 2015) towards the production

of advanced analytics. It should be noted that,

according to a report from Gartner, more than 30

percent of analytics projects is envisaged to deliver

insights based on a combination of structured and

unstructured data by 2015 (Gartner report, 2013).

The need for the development and adoption of

novel analytics tools is already identified from the

interested business stakeholders. Indicatively, it can

be noted that advanced analytics is the fastest-

growing segment of the business intelligence and

analytics software market (Gartner report, 2014).

According to the Intel’s annual IT business review

(Intel report, 2015), the use of business intelligence

and analytics tools increased Intel’s revenue by USD

351 million, while, based on a survey from the

International Data Corporation (IDC report, 2014),

the business analytics software market is expected to

grow at a 9.4% Compound Annual Growth Rate

(CAGR) over the next five years.

Fotopoulou E., Hasapis P., Zafeiropoulos A., Papaspyros D., Mouzakitis S. and Zanetti N..

Exploiting Linked Data Towards the Production of Added-Value Business Analytics and Vice-versa.

DOI: 10.5220/0005508700690080

In Proceedings of 4th International Conference on Data Management Technologies and Applications (DATA-2015), pages 69-80

ISBN: 978-989-758-103-8

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Nevertheless, in parallel with the development of

advanced analytics platforms, inhibitors towards the

integration of analytics tools and their daily usage

with smaller companies, such as the current

complexity in the usage of such tools and the

necessity for existence of specialized personnel,

have to be taken into account. Only if future

research succeeds in extracting information from

heterogeneous and distributed data in a user-friendly

and effective way, it can constitute the basis for

good operative and strategic business decisions and

projections.

Thus, it could be argued that the following two

challenges have to be faced towards the realization

of advanced analysis and evidence-based decision

making that optimize results for small scale

businesses and organizations: (i) the design of

advanced but user-friendly analytics tools that can

be easily integrated within the daily business

processes of organizations and enterprises and (ii)

the adoption of techniques that permit the production

and consumption of combined datasets that were

previously closed in disparate sources and can now

be appropriately interlinked.

Taking into account the aforementioned

challenges, in this manuscript we present an

approach for the exploitation of linked data

principles towards the production of added-value

business analytics, as it is developed within the

framework of the FP7 project LinDA (http://linda-

project.eu/). Linked data are about using the Web to

connect related data that wasn't previously linked, or

using the Web to lower the barriers to linking data

currently linked using other methods. They regard

actually a set of best practices for representing and

connecting structured information on the web, which

can be proven enablers towards the path to advanced

analytics.

The production of linked data analytics has two

notions in the proposed approach: (i) the interlinking

of datasets prior to the realization of analysis,

targeting at the preparation of datasets that can lead

to unexpected and unexplored insights that were not

possible previously and (ii) the interlinking of the

analytic results output and the analysed input

datasets for enriching the information at the input

datasets in a clean and straightforward way. Thus,

we refer to exploitation of linked data through the

production of added-value business analytics and the

exploitation of the produced business analytics for

the production of high quality five star linked data

(Zaveri, 2015).

The designed and developed business analytics

and data mining tool –including its functional

architecture and the developed ontology for the

support of linked data analytics- as well as a pilot

application scenario are presented. In more detail,

the structure of the paper is as follows: in section

two, the trends and challenges faced for the

production of advanced business analytics are

detailed along with the added value expected via the

production and consumption of linked data and the

positioning of the proposed approach with regards to

related work in the era; in section three the proposed

architectural and technological approach is

described; in section four a pilot application scenario

is presented while section five concludes the paper

by referring to the path towards the adoption of such

a tool by businesses as well as plans for future work.

2 BUSINESS ANALYTICS:

CURRENT TRENDS AND

NEEDS

2.1 Challenges and Trends

Advanced analytics are considered as a top business

priority, since it enables business organizations to

differentiate, significantly impact business

performance and enhance their position compared to

their competitors (Gartner report, 2014) (IBM

report, 2014). Towards this direction, a new class of

enterprises is emerging -called Generation D (Figure

1)- that routinely leverage varied data sources,

including structured and unstructured data, and

apply prescriptive analytics to optimise most

processes and undertake strategic business decisions

(IBM report, 2014). Thus, the current trend regards

the development of advanced and self-service

analytics tools that are going to facilitate enterprises

to harvest complex and heterogeneous data, turn

them into insights and make decisions.

As also stated in the introductory part of this

manuscript, a set of challenges exist and have to be

tackled towards the transition to a next generation of

analytics tools. Given that these tools have to be

made accessible by multiple end users –including

data scientists and decision makers- the overall

complexity in their usage, the overhead for the

consumption of complex data, as well as the

requirements for existence of personnel with

specialized skills have to be minimized.

Combination of data with sophisticated analytics

have to be supported, providing the capacity for

producing results leading to evidence-based answers

and high business value.

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

Figure 1: Evolution of enterprises with regards to analytics

(IBM report, 2014).

Furthermore, in order to support the production

of advanced analytics within the daily business

processes of enterprises, analytics tools have to be

extensible and support interfaces for interaction with

existing or newly developed business applications.

Business analytics have to lead to automation of

processes and decisions, provide the capacity for

identification of market trends, risks and new market

opportunities. Real time analytics have to be

supported in many cases, requiring access to the

available data and resources when they are needed.

Finally, scalability and performance issues have also

to be tackled, since in many cases there is a need for

processing of big data volumes or the production

and storage of big data analytics (Hu, 2014).

2.2 Why Linked Data Analytics?

2.2.1 Consumption of Linked Data for

Production of Business Analytics

Current analytics tools and data mining techniques

are mostly restricted to isolated or “silo” datasets

and therefore remain primarily stand alone and

specialized in nature (Lausch, 2015). In many cases,

such datasets impose limitations towards answering

complex and interdisciplinary questions that require

the establishment of association links among similar

entities and concepts that are present in different

datasets. In order to establish such associations, the

corresponding information has to be represented in a

format that is accessible and without any ambiguities

regarding its structure and semantics.

This is where linked data principles come. The

term linked data refers to a set of best practices for

publishing and interlinking structured data on the

Web. By following these practices, data from

diverse sources can use the same standard format

that allows them to be combined and integrated.

Linked data specify that all data will be represented

based on the Resource Description Framework

(RDF) specifications. Conceptual description of data

is realized based on specific vocabularies (and thus

semantics) accessible over HTTP, allowing the user

to interpret data from multiple vocabularies and

query them in a uniform manner.

By adopting linked data principles, a set of

advantages are provided towards the production of

advanced business analytics. Combination of data

from multiple and in many cases distributed sources,

as well as from publicly available data (e.g. open

governmental data) or privately owned data

maintained by enterprises, can help businesses

enhancing their experience of managing and

processing of data, in ways not available before.

Actually, linked data provide the capacity for

establishing association links among concepts in

different datasets, producing high-quality interlinked

versions of semantically linked web datasets and

promoting their use in new cross-domain

applications by developers across the globe. Such

interlinked datasets constitute valuable input for the

initiation of an analytics extraction process and can

lead to the realization of analysis that was not

envisaged in the past.

Moreover, interlinked versions of datasets may

be maintained or regularly updated facilitating the

provision of access to latest versions of the available

datasets, characteristic that is very helpful in cases of

data with high volatility. Interoperability and re-use

of linked data reduces significantly the data

management overhead imposed to data scientists for

maintaining up-to-date databases.

Linked data also provide mechanisms for

assessing the quality of the available data, since

there is a need to handle data with different quality

characteristics regarding their accuracy, consistency,

timeliness, completeness, relevance, interpretability

and trustworthiness. Data quality assessment is very

useful prior to an analysis, since it provides

information that can be helpful during the

interpretation of the analysis results (Zaveri, 2015).

2.2.2 Production of Linked Data through

Business Analysis

In addition to the business value produced through

the exploitation of linked data for the production of

business analytics, the interlinking of the analysis

results with the related input used for the analysis

has also a set of advantages.

Analytic maturity

Data breadth and sophistication

Traditional

Analytically

ambitious

Data-rich,

analytically enabled

Generation D:

Data-rich,

analytically driven

19%

31%

21%

29%

ExploitingLinkedDataTowardstheProductionofAdded-ValueBusinessAnalyticsandVice-versa

The value of the produced data is increased

since, by appropriately interconnecting them with

the input data, detailed information is provided to

analysts regarding the exact datasets used in the

analysis as well as the details of the process

followed during the analysis. In cases of analyses

that have to be updated in a periodical way and the

results of the analysis highly depend on the instance

of datasets used or in cases where comparison

among the analysis in diverse time periods is

required, such interlinking can provide further

business value to the available data.

Moreover, with linked data analytics, the

produced data are made discoverable for further

private or public use in the future by the enterprise

employees, clients or other data consumers. In case

of public data, other data publishers may also link

their data or analytics with them, adding further

business value. Actually, data are accessible and

usable at the point of creation and remain so

indefinitely. Quality of the produced data may be

also evaluated according to linked data evaluation

criteria while the evolution of the datasets may be

monitored.

2.3 State of the Art Analysis and

Progress beyond

Several open source data mining tools are available

today. The most commonly used include R (R

project, 2015), Weka (Weka tool, 2015), Knime

(Knime tool, 2015) and RapidMiner (RapidMiner

tool, 2015). A comparative analysis of such tools is

provided in (Hirudkar, 2013) and (Lausch, 2015) in

terms of supported functions, efficiency, hosting

platform needs, supported input formats, user

friendliness etc. Furthermore, in (Lausch, 2015), a

set of requirements are presented that have to be

fulfilled by data mining and analytics tools in order

to support future interdisciplinary research. These

requirements include the need for linking various

data mining types, the need for exploitation of

available open data stemming from various areas,

the need for use of open source software and the

need for use of data mining tools that do not require

a big time investment and in-depth programming

knowledge (Hirudkar, 2013).

The distinguishing characteristic of the proposed

approach in this paper –as compared to the state of

the art tools- is the introduction of the notion of

linked data analytics. It should be noted that – to the

best of the authors’ knowledge- no analytics

framework exists that supports consumption and

production of linked data (e.g. there is no support for

RDF as an input/output format). Focus is given on

the integration of existing open-source tools –

namely R and Weka- into a holistic platform that

facilitates the consumption and production of linked

data, taking into account and fulfilling the afore-

mentioned requirements for supporting future

interdisciplinary research (Lausch, 2015).

3 LINKED DATA ANALYTICS

ECOSYSTEM

In this section, we provide detailed description of the

proposed approach for the production and

consumption of linked data business analytics. As

the designed tool consists part of an ecosystem (the

LinDA workbench, available at

http://linda.epu.ntua.gr/) aiming to assist enterprises

in efficiently developing novel data analytical

services that are linked to the available public data,

in the first subsection we provide a short description

of the overall ecosystem. Following, in the second

subsection, detailed description of the business

analytics and data mining component is provided,

including its internal architecture and the supported

functionalities, the designed Linked Data Analytics

Ontology (LDAO) for the appropriate representation

of an analytics process combined with the

considered interlinking policy, the analytics

production workflow based on a categorization of

the supported algorithms, as well as the adopted

performance evaluation framework.

3.1 The LinDA Workbench and

Workflow

From a user perspective, the main LinDA workflow

can be summarized in three simple steps, as

illustrated in Figure 2. More specifically the three

workflow steps are:

Step 1 – Explore Datasets/Turn Data into

RDF: Using the LinDA toolset, users can publish

their data as linked data in a few, simple steps. In

cases where the data are not available in RDF

format, the users can simply connect to their

database(s), select the data table they want and make

their mappings to popular and standardized

vocabularies. LinDA assists even more by providing

automatic suggestions to the mapping process.

Based on the defined mappings, transformation from

various formats (e.g. csv, relational database) to

RDF is realised.

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

Step 2 - Query/Link Your Data: With the

LinDA toolset, users can perform simple or complex

queries through an intuitive graphical environment

that eliminates the need for SPARQL Protocol and

RDF Query Language (SPARQL) syntax. In

addition to the submission of queries, interlinking of

instances is supported, where the designer lets the

end user ignore its instance's data source and handle

instances as if they were defined in the same data

source. The possible types of interlinking vary

according to the interlinking element that is used.

More specifically, classes and object/datatype

properties can be combined in a versatile way,

during the interlinking procedure. Hereinafter, for

the sake of homogeneous representation, all

interlinking endpoints will be referred as interlinking

types. The interlinking of instances of the types [A]

and [B] can occur in several ways: (i) instances can

be interlinked directly to each other, in which case

an entity (URI) is fetched in the query results if

belongs to both types [A] and [B] at the same time;

(ii) an instance of type [A] can be interlinked to an

instance of type [B] via a property, where [A].p is

bound to be an instance of type [B] (“owl:same-as"

interlinking) and (iii) instances can be interlinked by

their properties, where [A].p = [B].q, given that

[A].p and [B].q refer to the same URI or that [A].p

and [B].q are literals (strings, numbers, dates etc.)

with the same value.

Figure 2: The LinDA Workflow.

Step 3 - Visualize/Analyse Your Data: the

LinDA toolset can help enterprise users gain insights

from the data that the company generates or

consumes through the support of a set of

visualization and analytics services. LinDA supports

visualisations over different categories of data, e.g.

statistical, geographical, temporal, arbitrary data, as

well as a largely automatic visualization workflow

for matching and binding data to visualizations. As

far as the analytics services are concerned, they are

presented in detail in the following subsection.

According to this workflow, the user can utilize

either external public data or internal, private

sources. If the initial data source is in RDF format,

the user can directly insert the data source to the

available data sources of the LinDA Workbench. If

the initial data source is in another format (relational

database, csv, etc.), the LinDA Workbench guides

the user to the toolset responsible for

transformations in order to transform the data into

the RDF format, with the utilization of popular

linked data vocabularies. Once in RDF, the user can

then visit the list of data sources and activate one of

the available LinDA services. More specifically, the

user has the option to a) visualize the selected RDF

data source, b) analyse it, c) query it and d)

edit/update/delete it.

3.2 Business Analytics and Data

Mining Component

The business analytics and data mining component

supports the realization of analysis based on the

consumption and production of linked data. A

library of basic and robust data analytic functionality

is provided through the support of a set of

algorithms, enabling enterprises to utilize and share

analytic methods on linked data for the discovery

and communication of meaningful new patterns that

were unattainable or hidden in the previous isolated

data structures.

3.2.1 Architecture and Analytics Workflow

The business analytics and data mining component

is based on an extensible and modular architecture

that facilitates the integration of algorithms on a per

request basis. The development of the component is

based on open-source software while integration of

algorithms is based on open-source analytics

projects.

The business analytics and data mining

component consists of the following sub-

components: the Query selection component, the

Algorithm selection and configuration component,

the Algorithm execution component and the Linked

data analytics management component, as they are

illustrated in Figure 3.

The Query selection component is responsible

for processing the output upon the execution of

simple or complex queries and loading it as input for

the initiation of an analysis process. For this

purpose, appropriate interconnection interfaces with

ExploitingLinkedDataTowardstheProductionofAdded-ValueBusinessAnalyticsandVice-versa

Figure 3: Business analytics and data mining component’s

architecture.

the tools that serve the design and execution of

SPARQL queries are designed and implemented. In

order to facilitate the user to select a meaningful and

coherent set of data, this component provides some

hints based on the existing usage of the dataset

within the LinDA workbench. The provision of such

hints may be helpful for the end users in order to

select the appropriate dataset and parameters for the

realisation of the envisaged analysis.

The Algorithm selection and configuration

component coordinates the process of suggesting,

selecting and configuring an analytics extraction

process in a user friendly and quite explanatory way.

Selection is realized based on a categorization of the

supported algorithms in classification, association,

regression/forecasting, clustering and geospatial

analysis algorithms. For each category, a set of

algorithms is supported along with their default

configuration and guidelines regarding their proper

usage. Based on the category of the selected

algorithm, a specific workflow is being followed by

the end user with guidance relevant to the

appropriate configuration of the algorithm

parameters’ setup. For instance, part of the

algorithms are based on one input dataset used for

the analysis, while another part necessitates the

existence of a training and an evaluation dataset (e.g.

in cases of classification analysis). In the latter case,

a further query is selected to be used for fetching the

evaluation data. Finally, the output format is selected

based on a list of supported formats per algorithm.

Upon finalizing the configuration phase of the

algorithm, the next step regards the usage of the

Algorithm execution component. This component

realizes the execution of the analytics process. It

should be noted that, at the current phase, integration

of algorithms from the Weka open-source tool

(Weka tool, 2015) and the R open-source project for

statistical computing (R project, 2015) is realized.

Thus, execution is based on the associated tools per

algorithm, while the overall process is transparent to

the end user.

The produced business analytics are then handled

by the linked data analytics management component.

This component is responsible for providing the

output of the analysis in the appropriate format (e.g.

RDF, text, graphs) in order to be meaningful for the

end user, as well as easily exploitable for further

usage (e.g. for visualization purposes). This

component realizes also the interlinking of the input

and output datasets, based on the defined

interlinking policy, as it is described in the following

subsection. Detailed information regarding the

analytic process realized, the queries/datasets used

for the analysis, a set of performance evaluation

metrics as well as access to the script used in the

associated open-source tool for the execution of the

algorithm is provided. The performance evaluation

metrics –as detailed in section 3.2.4- consist of a set

of quantitative and qualitative metrics. The first ones

are automatically computed by the data analytics

management component, while for the latter ones,

upon the execution of the analytics process, the user

is kindly suggested through a star-based

classification to evaluate the analytics result in terms

of meaningfulness, produced added value, quality of

experience etc. The computation of these metrics is

used towards the provision of a set of

recommendations to end users at the initiating phase

of future analyses.

Furthermore, within the business analytics and

data mining tool, customized information per user of

the tool is provided regarding the realised analysis in

the past. Such information may be proven helpful in

the interpretation of changes in the results (meta-

analysis of the analytics results), identification of

trends (e.g. which are the most popular algorithms

utilised by end users per business sector) as well as

identification of significant changes at the input data

(e.g. upon an update with newly collected data).

With regards to the technologies used for the

development of the business analytics and data

mining component, the front-end is based on the

Django Python Web Framework, while the Query

selection and the Algorithm selection and

configuration components are developed as a

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

Django module, pluggable in every Django

installation. The Algorithm execution and the

Linked data analytics management components are

implemented as a RESTful web service that is

consumed by the Django analytics module. The

aforementioned RESTful web service is

responsible for realising the analytics and data

mining part and offers a loosely coupled connection

with the overall Linda workbench, permitting its

easy integration to third party software. The Java-

based RESTful web service is developed with

RESTEasy, an implementation of the JAX-RS

specification and deployed by using a JBoss server.

3.2.2 Algorithms Categorization and

Integration

The supported algorithms in the business analytics

and data mining component aim to support a variety

of cross-sectorial studies and business needs. In

order to achieve it, a categorization of the supported

algorithms is provided along with the integration of

a limited set of algorithms per category, as shown in

Table 1. The supported set of algorithms per

category is based on the identified needs of the

LinDA pilot cases. However, as already mentioned,

the integration of further algorithms is supported,

especially from the Weka and R tools.

Table 1: Supported algorithms per category.

Category Supported Algorithms Tool

Classification J48 (decision making)

M5P (piecewise linear fit to the

dependent variable)

Weka

Association Apriori (decision making) Weka

Clustering KMeans - Partitioning

(unsupervised learning)

Ward Hierarchical Agglomerative

(unsupervised learning)

Model Based Clustering

(unsupervised learning)

Regression Linear/Multiple linear regression

(identify relationships and trends)

Weka

Forecasting Arima (market analysis trends and

seasonality patterns)

Geospatial Morans I (detect spatial

autocorrelation)

Kriging (describe spatial

autocorrelation)

NCF correlogram (describe spatial

autocorrelation)

The goal of classification analysis algorithms is

to find functions and models in order to identify to

which of a set of categories (sub-populations) a new

observation belongs, on the basis of a training set of

data containing observations (or instances) whose

category membership is known. The goal of

association analysis algorithms is to discover

interesting relations between variables in large

datasets. It is intended to identify strong rules

discovered in datasets using different measures of

interestingness (Piatetsky-Shapiro, 1991). The

identified relations can be quantified, while, by

using specific indicators (e.g. support, confidence

and lift values), the strength of the identified

associations can be evaluated. The goal of clustering

analysis algorithms is to group a set of objects in

such a way that objects in the same group (called a

cluster) are more similar (according to a set of

metrics) to each other than to those in other groups

(clusters). As far as the regression analysis

algorithms are concerned, the goal is to model the

relationship between a scalar dependent variable and

one or more explanatory independent variables.

Forecasting algorithms are also supported that

facilitate the process of making statements about

events whose actual outcomes (typically) have not

yet been observed. Finally, the goal of geospatial

analysis algorithms is to apply statistical analysis

and other informational techniques to data which has

a geographical or geospatial aspect.

As already stated, high priority is given to the

user friendliness of the provided interfaces based on

the design of specialized workflows per algorithm

category.

3.2.3 Linked Data Analytics Ontology and

Interlinking Policy

In order to support linked data analytics, an ontology

is being designed aiming at the representation of the

interlinking among input and output datasets, as well

as the conceptual representation of the overall

analytic process. Main benefits from the usage of

such an ontology include the consolidation of a

unified schema of terms and content relationships

that describe the business analytics realisation

process, the mapping of the acquired information in

this schema and the usage of queries to flexibly

investigate the available content and the capacity to

maintain quality and trace changes made over time.

The designed Linked Data Analytics Ontology

(LDAO) is based on a set of existing ontologies

along with specific extensions (LDAO, 2015).

Specifically, LDAO inherits concepts from the

Friend Of A Friend (FOAF) ontology (Brickley,

2014) and the PROV ontology (Lebo, 2013).

Concepts of Agent, Person, OnlineAccount and

Homepage from the FOAF ontology and concepts of

ExploitingLinkedDataTowardstheProductionofAdded-ValueBusinessAnalyticsandVice-versa

Figure 4: Linked Data Analytics Ontology.

Figure 5: Interlinking policy.

Agent, Activity and Entity from the PROV ontology

are used. Specific extensions are provided for the

representation of the analytics input and result node,

the analytic process along with performance

characteristics. The concepts and relationships

included in the LDAO ontology are shown in Figure

In addition to the design and specification of the

LDAO ontology, an interlinking policy is

implemented for the creation of linked data business

analytics. Different types of interlinking are

supported taking into account the peculiarities of

each type of algorithm, as illustrated in Figure 5.

These types can be classified in “one-to-one

relationship” and “many-to-one relationship”

interlinking. In the first case, there is “one-to-one

relationship” among each analytics result node with

an analytics input node, while in the latter case there

is “many-to-one relationship” among each analytics

result node with a set of analytics input nodes. In the

“one-to-one relationship”, we refer to classification,

geospatial and linear regression algorithms, while in

the “many-to-one relationship”, we refer to

forecasting and clustering algorithms. In both cases,

the input dataset can be derived from one or multiple

input data sources, depending on the executed

SPARQL query and the associated sources. In case

of multiple data sources, the direct relationship(s) is

realised on the interlinked data set, however

references to the input data sources are provided.

3.2.4 Performance Evaluation Framework

High importance is given on the evaluation of the

performance of the developed component with a

twofold objective: the evaluation of the efficiency

with regards to data management and algorithms

execution as well as the evaluation of the overall

usability of the component. It should be noted that

several studies exist for evaluating the performance

of data mining techniques (Hirudkar, 2013) (Jia,

2014). We are focusing on the differentiation in the

performance that is related with the consumption

and production of linked data within the analytics

process. The performance evaluation is based on an

assessment model that includes a set of quantitative

and qualitative metrics extracted mainly from the

ISO/IEC 25010:2011 standard (ISO/IEC 25010,

2011).

The quantitative metrics refer to a set of

indicators for evaluating the efficiency in handling

different linked data workloads per algorithm

category and the time required for the realisation of

the various steps within an analytic process. A

characterization of the data mining and analytics

algorithms based on their performance on handling

linked data is envisaged to be produced. The

following indicators are considered: time to fetch the

required linked data, time for execution of an

analytic process (per algorithm and per volume of

processed data) and time for producing the output

linked data (per algorithm and per volume of

produced data). Such indicators are accompanied by

system performance metrics such as processor

utilization, cache behaviour, memory access

statistics, and bus usage, considering that the

performance evaluation is realised in the same or

similar systems.

The qualitative metrics refer to a set of usage

metrics, such as effectiveness (accuracy and

completeness with which users achieve specified

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

goals), satisfaction (the degree to which users are

satisfied with the experience of using a product in a

specified context of use) and usability (the extent to

which a product can be used by specified users to

achieve specified goals with effectiveness,

efficiency and satisfaction in a specified context of

use).

4 BUSINESS ANALYSIS OVER

LINKED GOVERNMENTAL,

SOCIETAL AND HEALTH DATA

In this section, we provide description of a business

analysis realized through the implemented business

analytics and data mining component. The provided

analysis is indicative, related with governmental,

societal and health parameters and aims to prove the

validity of the designed solution. It should be noted

that a series of cross-sectorial analysis is envisaged

to be realized within the framework of the LinDA

project, based on the implementation of the LinDA

pilots (business intelligence, environmental and

media analytics sectors) as well as the usage of the

LinDA ecosystem from interested SMEs and

organizations. An overview of the analysis is

provided in the first subsection focusing on the

identified business needs, followed by the

description of the consumption and production of

linked data along with the primary analysis results in

the second and third subsections respectively.

4.1 Business Analysis Overview

The realized analysis regards the examination of the

relationship among governmental, societal and

health parameters across several areas in Italy. The

primary subject of the analysis is related with the

health impact of the intensification of the industrial

activity, taking into account the respect of the

existing laws and guidelines from regulatory

authorities during a set of years. The influence

imposed through the consumption of mass media

towards the public opinion’s formation and control

is also taken into account in the analysis.

Based on existing datasets –as they are detailed

in the following subsection- the business analysts

examine the evolution of industrial activity in

specific areas in relation with the rates of

carcinogens exposure violations monitored in the

various industries. The objective is to identify any

correlation among the industrial activity and its

impact on the health of workers. Since the industrial

activity should be related with development

indicators in a country and the realized violations

could be also related to corruption levels (e.g.

through greater tolerance by public officers to

violation instances), examination of the variation in

the collected data based on changes on the Gross

Domestic Product (GDP) growth rate and corruption

indexes are examined.

Locality characteristics are also taken into

account in the study, from various perspectives. On

one hand, the incident of violation of the exposure

limits in carcinogens in a region is examined in

relationship with the particular political party in

power in this area. On the other hand, given the

power that the mass media have in modern societies,

the analysis includes examination of the relationship

that may exist among the high consumption of mass

media and the political parties that are dominant per

geographical area in Italy. Last but not least, the

number and type of the industries that are operating

close to the areas with high number of carcinogens

exposure violations are examined. The identification

of such relationships may help the business analysts

or the public officers to propose holistic solutions

for reducing the negative health impact that the

industrial activity might have.

4.2 Linked Data Consumption

The input datasets for the realization of the analysis

are provided from a diverse set of sources, as shown

in Table 2. Interlinking of datasets is realized based

on the location and time parameters in order to

prepare unified datasets, ready to be fed as input to

the analysis process. An indicative example of

interlinking is shown in Figure 6.Further interlinking

may be realized by the business analysts for the

collection of information from available web

resources, such as information related to a specific

Italian province (e.g. information from DBpedia),

the positioning of the members of the national or the

European parliament of a specific political party

(e.g. information from the datasets of the Italian

parliament - http://data.camera.it/data/en/datasets/-

or the European parliament -

http://www.talkofeurope.eu/data/) or the number and

type of the industries that are operating close to

specific areas (e.g. information via

http://www.openstreetmap.org).

ExploitingLinkedDataTowardstheProductionofAdded-ValueBusinessAnalyticsandVice-versa

Figure 6: Preparation of interlinked datasets for analysis.

Table 2: Data sources and parameters description.

Data Source Parameter Description

Data

access

Istat

(http://linkedstat.spa

ziodati.eu/)

Political parties leadership &

mass media consumption per

Italian region (2005-2012)

SPARQL

endpoint

Inail

(http://dati.inail.it/o

pendata/elements/R

egistriEsposizione)

Number of companies that

surpass the established

exposure levels to carcinogens

subjects (1994-2012)

RDF

dump

Worldbank

(http://worldbank.27

0a.info/sparql)

GDP growth rate and

corruption levels (1996-2008)

SPARQL

endpoint

Linked Geodata

(http://linkedgeodat

a.org/sparql)

Industrial sites per region

(current status). The query is

realised based on the

http://www.openstreetmap.org

project.

SPARQL

endpoint

4.3 Primary Analysis Results and

Linked Data Production

The first part of the analysis regards the examination

of the impact that the GDP growth rate and

corruption indexes variation have to the total

carcinogens exposure violations in country level. A

multiple linear regression was realized for this

purpose -having as dependent variable the number of

violations and as independent variables the GDP

growth rate and corruption indexes- resulting though

to a high p-value and not permitting the extraction of

conclusions regarding potential associations among

the examined variables. Then, the M5P algorithm

was executed for producing a piecewise linear fit to

the dependent variable. Based on the produced

results, it seems that high GDP growth rate values

(GDP growth rate > 1.708) are associated with

smaller number of violations (55 instances), while

low GDP growth rate values (GDP growth rate < -

0,095) are associated with high number of violations

(from 1239 to 3305 instances). When the GDP

growth rate fluctuates between these values (-0.095

≤ GDP growth rate ≤ 1.708), the trend followed

depends on the corruption levels. An increase in the

corruption levels leads to an increase in the number

of the violations, as it could be envisioned. The

lowest number of violations is given for the

following combination: GDP growth rate ≤ 1.708

and 4.25 < corruption rate <= 4.75.

The second part of the analysis regards the

relationship among the consumption of mass media

and the political preferences of people per Italian

area. A clustering analysis is realised leading to the

following highlights. Mass media consumption and

political party preferences are highly correlated, as

they are grouped in less and well defined clusters in

comparison with the political party preferences and

the Italian province variables or the mass media

consumption and Italian province variables, as

illustrated in Figure 7. Furthermore, it is noted that

the provinces associated with lower consumption of

mass media vote in a more differentiated way. As

shown in Figure 8, in regions with high consumption

of mass media there is a dominant cluster (the red

one) where its instances are mostly composed by the

dominant political party, while in the case of regions

with low mass media consumption, the red cluster is

smaller and the rest of them are more intensely

represented, as illustrated in Figure 9.

Figure 7: Clustering analysis results.

Figure 8: Clustering of political parties’ preferences to

regions with high mass media consumption.

DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications

Following, an instance of the RDF files of the

produced linked data analytics through the execution

of the aforementioned clustering analysis is shown

in Table 3. The interconnection of the analytics input

and output files based on the concepts and

relationships defined in the LDAO ontology is

provided.

Figure 9: Clustering of political parties’ preferences to

regions with low mass media consumption.

Table 3: Instance of linked data analytics.

Indicative Input Dataset:

<rdf:Description

rdf:about="http://linkedstat.spaziodati.eu/dataset/61_132/

ITE4/TOTAL/20/9/9/1/A/2000">

<rdf:type

rdf:resource="http://purl.org/twc/vocab/conversion/RawConv

ersionProcess"/>

<dbpedia-owl:media>90.7</dbpedia-owl:media>

<dbpedia-owl:place>Lazio</dbpedia-owl:place>

…………

</rdf:Description>

Analytics Result Node:

<rdf:Description

rdf:about="http://linda.epu.ntua.gr:8000/analytics115V2Dat

e15022015#/81">

<rdf:type

rdf:resource="http://linda.epu.ntua.gr:8000/analyticsontol

ogy#analytics_result_node"/>

<analytic_process:predictedValue>3</analytic_proce

ss:predictedValue>

<prov:wasGeneratedBy

rdf:resource="http://linda.epu.ntua.gr:8000/analyticsontol

ogy#analytic_process/115/2"/>

<prov:wasDerivedFrom>http://linda.epu.ntua.gr:8000

/analyticsontology#analytic_input_collection/115/2/3</prov

:wasDerivedFrom>

<rdfs:subClassOf

rdf:resource="http://www.w3.org/ns/prov#Entity"/>

</rdf:Description>

Analytics Input Collection

<rdf:Description

rdf:about="http://linda.epu.ntua.gr:8000/analyticsontology

#analytic_input_collection/115/2/3">

<rdf:type

rdf:resource="http://www.w3.org/1999/02/22-rdf-syntax-

ns#Bag"/>

<rdf:_1

rdf:resource="http://linkedstat.spaziodati.eu/dataset/61_1

32/ITF4/TOTAL/20/9/9/1/A/1998"/>

…………

<rdf:_3

rdf:resource="http://linkedstat.spaziodati.eu/dataset/61_1

32/ITE4/TOTAL/20/9/9/1/A/2000"/>

</rdf:Description>

<rdf:Description

rdf:about="http://linkedstat.spaziodati.eu/dataset/61_132/

ITE4/TOTAL/20/9/9/1/A/2000">

<rdf:type

rdf:resource="http://linda.epu.ntua.gr:8000/analyticsontol

ogy#analytic_input_node"/> </rdf:Description>

5 CONCLUSIONS AND FUTURE

PLANS

By taking into account current business trends and

challenges towards the production of advanced

business analytics that can lead into insights and

facilitate businesses to make analytical-driven

decisions, an approach for exploiting linked data

towards the production of added-value business

analytics has been provided.

The approach is based on a designed and

developed business analytics and data mining tool

that facilitates the consumption of linked data for the

realisation of analysis, as well as the production of

data analytics interlinked with the input data in the

analytics process making them discoverable and

further exploitable in the future. The functional

architecture of the proposed approach, the designed

ontology for the representation of the defined

concepts and relationships as well as a pilot

application scenario for validation purposes are

presented in detail.

Based on the provided results, it could be argued

that the proposed approach can help enterprises

enhancing their experience of managing and

processing of data, in ways not available before. It

can provide them the potential to produce advanced

knowledge, leveraging the power of linked data

analytics, acquire a significant competitive

advantage in the decision making process and

increase their overall effectiveness. However, in

order to be able to easily adopt and integrate the

usage of such an ecosystem in their daily business

processes, they have also to take into account the

need for an initial learning curve as well as the

involvement of data scientists in the specification of

the analysis and the interpretation of the analysis

results.

With regards to open issues for further

investigation, a set of cases are identified. These

include the need for realising a detailed evaluation

of algorithms efficiency with regards to the usage of

linked data taking into account a wide set of

algorithms per category, the performance evaluation

of the proposed business analytics and data mining

tool based on the defined performance evaluation

framework, the tackling of challenges related to the

management of big data and the adoption of a

distributed nature of the execution mode as well as

the implementation of a set of cross-sectorial use

cases aiming at the validation of the overall

approach and the identification of the produced

business value in each case.

ExploitingLinkedDataTowardstheProductionofAdded-ValueBusinessAnalyticsandVice-versa

ACKNOWLEDGEMENTS

This work has been co-funded by the LinDA project,

a European Commission research program under

Contract Number FP7-610565.

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DATA2015-4thInternationalConferenceonDataManagementTechnologiesandApplications