Analysing E-Business Applications with Business Provenance

Sergio Manuel Serra da Cruz

, Laci Mary Manhães

, Raimundo Costa

and Jorge Zavaleta

Departamento de Matemática, Universidade Federal Rural do Rio de Janeiro, Seropédica, Brazil

Universidade Federal Rio de Janeiro, Ilha do Fundão, Brazil

Keywords: Business Provenance, Web Services, SOA, E-Business.

Abstract: Business Provenance provides important documentation that is an essential to increase the trustworthiness

and traceability of end-to-end business operations. This paper presents two data marts that allows

multidimensional analysis of business provenance metadata collected from a real e-business scenario.

Provenance was collected with the aid of an architecture named BizProv. We conclude the paper with the

identification of the challenges that will drive future research of BizProv.

1 INTRODUCTION

Provenance is a broad topic that has many meanings

in different contexts. Business provenance is essential

to determine whether information is trusted, to

integrate varied information sources and to give credit

to originators when reusing information. Curbera et

al. (2008), Roznyai et al. (2011) and Cruz et al.

(2012) are investigating how business provenance

increases the trustworthiness and traceability of end-

to-end business operations in a dynamic, flexible and

cost effective way. Business provenance is a form of

metadata artifacts used to increase the effectiveness of

the business operations, to manage risks and to

provide a deeper visibility into business processes. It

is achieved by capturing and registering history and

context related to specific business and operational

goals (Fox, Huang, 2008).

Business provenance provide information about

processes, resources, data and users’ operations,

supporting analyses concerning business processing

as well as QoS (Quality of Services). Business

provenance has a pragmatic and interesting

advantage, as it can be used for QoS (historic of

errors, failure rates or average response times),

compliance, trust and auditing purposes it may also be

important for monitoring the application when in

production. In short, business provenance may be

molded in a consistent and trustworthy way with other

sources of information to accelerate a broad range of

business objectives.

To provide an improved understanding of the need

for business provenance and its importance on

business processes based on SOA applications we

have presented BizProv in a previous work (Cruz et

al., 2012). It was designed as a loosely-coupled

architecture used to collect the retrospective business

provenance generated by business processes

implemented as Web services. In this paper we show

how to model and query multidimensional

provenance data cubes with the provenance metadata

previously gathered with aid of BizProv. The

analytical provenance queries can be used to monitor

the QoS of business operations.

The rest of this paper is as follows. In section 2

presents a short background on business provenance.

In section 3 discusses two business provenance star

schemas. Finally, in the section 4, we conclude our

paper and provide an outlook for future work.

2 BACKGROUND

Provenance-related technologies help to understand

what actually happened during the lifecycle of a

business process by examining how data was

produced, what resources were involved and which

tasks were invoked (Curbera et al., 2009). There are

two forms of provenance: prospective and

retrospective (Freire et al., 2008). Prospective

provenance captures the speciﬁcation of processes; it

corresponds to the steps that need to be followed to

generate a data product. Retrospective provenance

captures the steps that were executed as well as

information used to derive a speciﬁc data product. As

far as we are concerned, business provenance is a

297

Manuel Serra da Cruz S., Mary Manhães L., Costa R. and Zavaleta J..

Analysing E-Business Applications with Business Provenance.

DOI: 10.5220/0003967902970300

In Proceedings of the International Conference on Data Communication Networking, e-Business and Optical Communication Systems (ICE-B-2012),

pages 297-300

ISBN: 978-989-8565-23-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

kind of retrospective provenance.

BizProv is a loosely-coupled architecture designed

to collect the retrospective business provenance

generated by distributed business processes. It allows

business analysts to incorporate other sources of data,

such as Web logs and clickstream to get deeper

visibility about business processes. BizProv was

conceived as a two layered architecture. The first

layer consists of a composite Web services provider,

basic Web services provider, E-Probe Module, two

active intermediaries, a provenance repository which

was conceived take into account the latest

recommendation of Open Provenance Model (Moreau

et al., 2011). The second layer is composed by a web

server log parser, an ETL tool and the OLAP

provenance data cubes. More details about BizProv

architecture and its provenance schema can be found

at Cruz et al. (2012).

3 OLAP PROVENANCE CUBES

Business provenance is inherently multidimensional;

it can be analyzed from multiple perspectives. The

BizProv´s OLAP Provenance Cubes are structures to

support analytical provenance queries in high-

dimensional space. The design of Provenance Cubes

requires addressing a variety of issues including a

novel set of OLAP dimensions, measures and also

defining the semantics of OLAP operations over

provenance cube. Last but not least, OLAP is a

technology widely used by enterprises and can be

distributed to business analysts using a variety of

platforms. For such reasons, despite of the existence

of other initiatives to query provenance metadata,

such as (Curbera et al., 2009, Lakshmanan et al., 2011

and Cruz et al., 2009). Our approach uses the

analytical processing to query business provenance

metadata.

This section shows how business analysts can gain

insight into business provenance metadata through

fast, consistent, interactive access to a wide variety of

possible views of information. One of the uses of

Web services´ provenance is to track QoS aspects of

Web services utilization. Such tracking provides

important feedback to service management.

3.1 A Business Use Case

From business provenance repositories generated by

BizProv and other sources of data (such as

clickstream and Web server logs) an ETL tool may

process and populate a database specially designed for

analytical purposes. Provenance metadata used in this

section was gathered from some Web services of a

virtual books retailer operating on the Web

Such database is usually structured according to a

star schema (it is a logical arrangement of tables such

as the entity relationship diagram without

normalization of tables (Kimball et al., 2004)), where

data are described in terms of facts, (measures of

interest to be analyzed) and dimensions (perspectives

under which the facts are analyzed). Thus, a

provenance OLAP cube is a set of metadata, with

distinct granularity, that is organized and structured in

a hierarchical and multidimensional arrangement to

allow analysts to perform ad hoc queries over

provenance repositories. The next sub-sections

provide two general star schemas based of the

provenance collected by BizProv architecture. The

first schema (Figure 1, at the end of the paper) may be

extended according to other needs, including

additional information such as service reputation

(used to obtain basic insights about the user

experience dealing with the Web services). The

second schema (Figure 2, at the end of the paper)

aims at capturing users’ interactions to support and to

complement business processes analyses.

3.2 Monitoring Web Services QoS

The schema, depicted in Figure 1, can be used to

investigate several QoS issues about the business

processes, such as ResponseTime, MessageSize and

InitializationTime. In order to investigate QoS,

different dimensions may be used to provide

consolidated queries upon these variables. For

instance, the Status dimension supports queries

concerning reliability and availability. For example,

selecting the total amount of services requisitions in

Dec 2011, by Web service method which ended with

a Status different from “OK”, divided by the total

amount of services requisitions (by Web service

method) in Dec 2011, gives us a Web service method

reliability indicator. This information may be used as

a basis for Web services code maintenance. Note that

we have included a service requisition attribute in the

fact table, just for convenience, for the sake of queries

readability, as suggested by Kimball et al. (2000,

2004). Its value is always 1.

The Origin dimension supports queries

concerning service misuse. For example, if the total

amount of a Web service requisition from a given

origin in an hour is much larger than the others, it

may be someone, by mistake or not, repeatedly

calling the service in a loop. This information may be

used for user suspension or warning. The Date and

Time dimensions are role playing dimensions

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(Kimball et al., 2004) and should be used in

conjunction with others, to limit the scope of analysis

in time.

The Web service dimension is a key dimension

and supports the other dimensions involved in the

analyses. It is useful for providing comparative

performance information when used with the Host

and Process dimensions. We can have, for example,

the average response time by a business processes

implemented as a Web services as a hint for possible

poor Web services performance. If one Web services

average response time is far above the others and all

the Web services from the same host have

approximately the same average response time,

perhaps we should blame the Web services host and

warn the Web services provider. This might be

especially useful if we were collecting provenance of

Web services calls at the basic Web services provider.

The Method dimension defines the grain of the

QoS analysis and it should be used in conjunction

with the other dimensions. The MethodVersion

attribute controls the versions of the Web service

Method and Web services dimensions we modeled as

slowly changing dimensions (Kimball et al.., 2004),

since we might be interested in monitoring eventual

performance deterioration or improvement due to new

service’s versions.

The Priority dimension may be used in

conjunction with Date and Time dimensions to

analyze if a Web services method was called with

priority and has improved its execution time when

compared with the same services method non-priority

calls. It is important to observe that priority calls

usually have higher prices.

3.3 Monitoring Sales

The Sales fact table considers two key variables:

SellingPrice and ProductSales, as illustrated in Figure

2. These variables may vary according to the domain

being analyzed. The Origin dimension may provide

answers concerning customer´s profile. For example,

which customers spent more money over a given

period of time?. This information may be used for

customer’s special treatment as, for instance, Web

service’s method call with priority when a profitable

customer is involved.

The Web service dimension is useful for providing

comparative selling information when used with the

Product dimension. One can have, for example, what

Web service of a given category is linked to low

selling rates. Tabulating such information with other

data provided by the QoS features, like the service

provider, may indicate a reason to change the Web

service by another, perhaps from other basic Web

services provider. Note that we have included the

ProductSales attribute, whose value is also always

equal to 1, as we did on the QoS fact table.

The Causal dimension may provide hints to

indicate the occasion which lead the customer to buy

a product. Causal dimensions were first proposed by

Kimball (2000, 2004). Although we cannot assure

which was the real cause, we can at least estimate. For

example, if a product has a price discount in June, this

may be a good reason to justify its good selling

performance in that month. Also, if a product may be

purchased in ten installments with no interest taxes in

May, as its payment type indicates, this may lead to

product selling increase. Besides, if the product has

been advertised in the Web page or on the

newspapers, and the product selling increases, this

may indicate that the advertisement succeeded.

Another selling’s increase may come from

PackageType, i.e., when two products are sold

together. For the sake of simplicity we don’t track

here which products are combined in the sale.

The Product dimension actually defines the grain

of the sales fact table, and is the basis of detailed

analyses that most of the times involve other

dimensions. The Web Page dimension provides

information about the product’s context of

acquisition. We assume that products may be acquired

in more than one page. This dimension may help

answer the question: “In which page of the Web site a

product has better sales?”.

4 CONCLUSIONS

Organizations that know where they are in terms of

analytics adoption are better prepared to turn

challenges into opportunities. Thus, analyzing

business provenance may represent a key competitive

advantage. The work presented in this paper is part of

a larger, long-term research effort aiming at

developing a native provenance-based service-

oriented BPM platform.

Future research efforts may focus on other fields

such the Agriculture (the investigation of online

payments for agricultural products and subsidies may

help governments better predict food production

trends and incentives). This knowledge can be used to

ensure the availability of proper crop storage, reduce

waste and spoilage, providing better information

about what types of financial services are needed by

farmers.

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Figure 1: OLAP Provenance QoS Cube.

Figure 2: OLAP Provenance Sales Cube.

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