BUSINESS INTELLIGENCE THROUGH REAL-TIME TRACKING

Using a Location System Towards Behaviour Pattern Extraction

Pedro Abreu, Vasco Vinhas

FEUP - Faculty of Engineering of the University of Porto, R. Campo Alegre 1021, Porto, Portugal

DEI - Department of Informatics Engineering, R. Campo Alegre 1021, Porto, Portugal

LIACC - Artiﬁcial Intelligence and Computer Science Laboratory, R. Campo Alegre 1021, Porto, Portugal

Pedro Mendes

FEUP - Faculty of Engineering of the University of Porto, R. Campo Alegre 1021, Porto, Portugal

DEI - Department of Informatics Engineering, R. Campo Alegre 1021, Porto, Portugal

INESC Porto - Institute for Systems and Computer Engineering of Porto, Rua Dr. Roberto Frias, 378, Porto, Portugal

Keywords:

Location System, Real-Time, Knowledge Extraction, Business Intelligence.

Abstract:

Nowadays, tracking systems constitute an important knowledge support in order to compute important mea-

surements in companys processes efﬁciency. As consequence of that, this project proposes a methodology and

an application, based on a tracking system to obtain, by automatic means, dynamic location data on items.

This solution assumes that the client carries or drives an item of some kind. In each item there is an identify-

ing tag attached and hidden in order to make the item at hand detectable by all the sensors that are scattered

around the area. Because of the fact that the tag is light and hidden and also has no information regarding

the speciﬁc person/agent this process is completely transparent to the client or robot that is being implicitly

tracked. This system produces real-time shop ﬂoor visualization maps with intelligible data on online item

localization; individual item complete path routes; online and historical population density rates and path

routes concentration; and also item vision enabled concentration maps as emulation for item omnidirectional

vision considering occlusions. This proposed system might be useful in many different areas, for instance in

a traditional retail environment tracing clients through a commercial area or enabling item tracking and route

analysis in a hospital.

1 INTRODUCTION

Detecting behavioral patterns is a challenging task

that marketing and distribution companies face. The

issue has been addressed through the past years on

several perspectives like deterministic psychology

(Luce, 1999; Choustova, 2007). However due to their

active consciousness, human beings are extremely un-

predictable and so these methods failed to provide any

accurate data that could be used for industrial pur-

poses. Having these approaches been unsuccessful,

statistical inference with large data sets is still one of

the most powerful tools available.

Nowadays, tracking systems can represent a pow-

erful tool to support monitoring activity. With these

systems, some performance measurements on com-

pany process efﬁciency can be obtained regardless

of the speciﬁc tracked target. This research work

presents a methodology and a tool, based on a track-

ing system to obtain by automatic means, movements

data on these elements.

The presented solution assumes that the client car-

ries or drives an item of some kind, inside the space.

It is also assumed that these items are outside the en-

try of the traceable space, although already inside of

its admissible space. In each of the items there is an

identifying tag attached and hidden in order to make

the item at hand detectable by all the radars that are

scattered around the area. By detecting it, the client

or robot is being implicitly tracked in a completely

transparent way for him/it since the tag is light and

hidden and also has no information regarding the spe-

ciﬁc person/agent.

Several beneﬁts can be withdrawn from using a

system such as this. Instantaneously one could use it

to monitor the traceable area in a more effective way

Abreu P., Vinhas V. and Mendes P. (2008).

BUSINESS INTELLIGENCE THROUGH REAL-TIME TRACKING - Using a Location System Towards Behaviour Pattern Extraction.

In Proceedings of the International Conference on e-Business, pages 51-57

DOI: 10.5220/0001909900510057

 SciTePress

than looking at dozens of screens with images from

security cameras. Live monitoring of the elements’

positions on a speciﬁc area allows managers to iden-

tify congested sectors. It also allows him to identify

hot and cold zones which may be related to the inter-

ests points at hand or, for instance, to a local security

issue or other type of event such a medical emergence.

Regarding long term data analysis the advantages

of using such a system are several and may be more

than the ones presented next. By analyzing all the

paths taken by the elements, it is possible to obtain the

hot zones on any time frame and thus evaluate the suc-

cess rate of a given promotional campaign and among

other things. Erroneous and random movements may

also be correlated with a security issue, and thus this

system could also represent an interesting addition to

conventional security systems. The results later ob-

tained would clearly point out the success of the lay-

out redesign. Of course these last measures imply cor-

relating client positions with goods bought by him.

The paper is structured as follows: section 2 de-

scribes the current state of the art regarding the sev-

eral areas of knowledge involved in the development

of the system described in this paper. Section 3 de-

scribes the tool that simulates the environment as well

as its architecture and core functionalities. Section 4

discusses the results obtained so far and the next sec-

tion concludes the paper by summarizing the focus

of this research work and pointing out future lines of

discussion.

2 STATE-OF-THE-ART

Nowadays, tracking systems represent an important

research area as their applications are transversal to

several areas of knowledge (e.g computer science,

medicine, simulation, robotics as well as industrial

tracks). In the past few years, technology has evolved

in order to provide more accurate measurements. In

the robotics area, for better modeling the world, it is

extremely relevant to accurately process the signals

received by the multiple sensors involved. Locating

objects of the real world to the modeled one is a criti-

cal task for the appliance of the navigation algorithms

and methodologies. Following these advances the

work published by Hyunwoong Park (Park H., 2006)

presents a new kind of sensor system with multiple

rotating range sensors. Such system allows a robot

to guide itself on a priori unknown world. On the

other hand these tracking systems also ﬁnd interesting

applications on scenarios where the context environ-

ment is already known. Regarding this last system,

locating elements assumes a crucial role. To achieve

this goal, several technologies have been used. By

doing a brief comparison, it is observable that all of

them have their strengths and ﬂaws concerning char-

acteristics like the cost in terms of initial investment

and maintenance. There are others related to environ-

ment speciﬁcities. Among these last, other parame-

ters such as coverable area, tracking detection errors

and occlusion problems should also be considered.

One of the most effective technologies is also one

of the most expensive ones and concerns detection of

thermal signatures. This technique is appropriate to

living organisms which emit particular heat waves.

One particular application of this technology is the

monitoring of the fauna in the ocean (Raizer, 2003).

Another interesting technology is Bluetooth because

most modern mobile equipments are prepared to send

a receive data though this protocol. Although the

initial investment is low the coverable area is not

very wide and battery consumption is high, in relative

terms (Jappinen P., 2007).

The cheapest solution is infrared based. Even

though its price attractiveness, infrared systems tend

to fail on most real environments because the signal

is unable to reach the target if there is an opaque ob-

ject between the receiver and the target (Krotosky J.,

2007).

Two of the most emergent technologies for track-

ing are RFID and Wi-Fi based. The ﬁrst one still lacks

standardization which is somehow reﬂected in the

pricing of both receivers and transmitters. It is based

on high frequency radio waves having the detectable

tags a passive or an active response. Passive tags are

only detectable on a 13 meter radius and are used

for instance on the new USA passports. Active tags,

alternatively, are detectable on a much wider range

but are more expensive (around 400%) essentially be-

cause the tags require an independent power supply

(Chao C., 2007). Wi-Fi may also be considered as a

tracking technology. This approach is mainly used for

creating wireless computer networks but in this case

the involved tracking only requires the usage of the

low level protocols. This type of solution is interest-

ing because it makes possible reusing existent com-

puter networks for other proposes and takes advan-

tages from possible simple detection with at least one

access point. With only one access point the system’s

precision may not be very high but there is no need

for triangulation. Occlusion problems and signals

losses, with the use of this technique will be reduced

to a residual level in both open spaces and indoors

– considering that indoor spaces do not have signiﬁ-

cant metal structures within the walls) (Mingkhwan,

2006).

Another area that suffered several developments

ICE-B 2008 - International Conference on e-Business

in the past few years concerns world modeling. In

this research area there are relevant research topics,

most of them related to computer graphics. Most

of the current advances focus on three dimensional

(3D) worlds. In this scope, the evolution on com-

puter graphics is the most notorious. Nowadays, sim-

ple systems are able to represent complex 3D world

including high resolution textures, detail animation

(Vazquez, 2007) and weather condition (Grudzinski,

2007). It is even possible to recreate a 3D world from

textual speciﬁcations (Moura J., 2004). On top of

3D world, many algorithms are applicable in other to

optimize rendering performance and obtaining world

data such as visible objects of a certain point.

These algorithms are too complex for most real-

time tracking systems and therefore for this research

work the world is assumed to be a two and a half di-

mension one (2,5D). In this scenario, a map is rep-

resented considering a bird’s eye view and assuming

that the height of the objects has no maximum value.

In these conceptions it is simpler to obtain the set

of potentially visible objects from a certain point us-

ing a portal culling algorithms; that consider walls as

complete occluders and assigns a vision probability to

each region in the map (Pires, 2001). Such is achieved

by dividing the indoor space into separate sectors and

then portals which represent the breaches between the

sectors. By drawing cones that connect the observer

point and both extremities of a given portal, one can

obtain the areas where all the objects are potentially

visible. Some caution is required when performing

such an operation, since the lines that represent the vi-

sion cone cannot intersect the ones representing zone

divisions. It is also relevant to state that one must as-

sume the observer’s vision direction is the center of a

given ”vision cone”.

3 PROJECT DESCRIPTION

The project description is divided of three distinct

subsections. In the ﬁrst denominated as Project Ar-

chitecture a description of the system’s architecture

is depicted. In the Real Time Tracking Visualization

and Concentration Maps subsection the principles of

the system features is explained and in the last sec-

tion, Client Vision Module the used vision algorithm

is exposed.

3.1 Project Architecture

This research work proposes a decentralized architec-

ture and prototype tool following that same principle

as detailed in Figure 1.

Figure 1: System’s Architecture.

In the ﬁrst stage, using a location system that might

be instanciated in a RFID or Wi-Fi based solution,

covering an area of for instance a large open space or

building, with a maximum error of one meter, move-

ment data is collected regarding the tagged elements

present in the given speciﬁc ﬂoor. In order to gather

the location data one agent has been developed to col-

lect all the positions. This action has a given periodic-

ity and is dependent on the location engine. Typically,

this collected data is guaranteed to be obtained every

second at most for every single tag, although this ﬁg-

ure might be decreased depending on the number of

simultaneously trackable objects.

After the collection process the agent sends the in-

formation to the server application. Prior to this ac-

tion, the agent executes a simple, yet efﬁcient, data

validation that is based on the map of the structure

that is sent by the server before the collector agent

boot process.

The server application, before being able to re-

ceive any position data, must load the ﬂoor map that

will contain the trackable objects, from a XML ﬁle.

The maps are modeled as 2,5 D worlds and include

several structures that can be easily adapted to many

types of spaces. Before using it, the server validates

the map against a XML Schema.

For instance, a XML ﬁle representing a traditional

retail shop includes entry areas and an exit one that

in this case is designated as a payment area. There

are also walls that have inﬁnitive height positioned

around the map. In the supermarket example these

last are named as shelves as can be seen on Figure

2. The proposed XML structure allows specifying a

color and a designation for each half of the wall. The

half is determined by the largest dimension, and in

case of a square it is assumed to be vertically alligned

as can be seen on Figure 2.

For each set of received coordinates, the server

stores them in a database for universality sake. The

BUSINESS INTELLIGENCE THROUGH REAL-TIME TRACKING - Using a Location System Towards Behaviour

Pattern Extraction

Figure 2: Drawn Map with Corresponding XML File Struc-

ture.

system executes it in a completely completely agnos-

tic way concerning the database provider. At this

point it is also relevant to state that the server is a

multi-threaded application – having a thread per tag.

Each thread writes into a reserved memory location

the tag’s current position. The previous position is

not overwritten; instead it is stored in the database.

Each thread includes some recovery proceedings such

as deleting all the records of a speciﬁc tag in a given

time frame if it stops transmitting its location for a

long period of time regarding the context at hand.

As each thread executes, the server GUI is able to

display online data. This data represents where the

tags are located on the map. Other types of views in-

volve processing the data in real-time, using computer

memory and/or by consulting the database registers

for further in depth reports (section 3.2).

3.2 Real-Time Tracking Visualization

and Concentration Maps

The Server GUI includes several different views of

both the online and historical data. In all these views

there is a visual representation of the map. In order

to draw a map the server requires to systematically

perform a scale transformation involving real world

coordinates and pixel coordinates. This kind of trans-

formation must be dynamic because, in any instance,

the GUI can be resized. When the view involves po-

sitioning tags on the map this transformation is also

applied to their centre positioning.

The simplest view allows representing online the

tags in their actual positions. Other views are ob-

tained through the server’s knowledgment extraction

features. The Zone Matrix consists in determining

in real-time which are the most and least populated

zones, denominated as hot and cold zones.

It is also possible to consult this data on a wider

time frame considering the same space with or with-

out the same layout. This last feature requires

database access. The zones are automatically ob-

tained by dividing the space into a grid with ﬂexi-

ble dynamic resolution. This dynamic division allows

both a more in depth study of the hot and cold zones

and also a less detailed one in order to study, for ex-

ample, the spaces quadrants occupation.

Several other results are obtainable by accessing

memory-based data structures for limited time frame

analysis and by querying the database in similar

modes. It is possible to obtain historical client paths,

the shop areas walls, shelves or objects that were

more observed by the clients.

In order to reproduce historical client path recog-

nition all the clients coordinate are stored into the

database with a timestamp that is related to a given

map. Figure 3 exposes the paths taken by several

clients in a given time frame.

Figure 3: Paths taken by the Elements.

3.3 Client Vision Module

The client vision module uses a simpliﬁed version of

the occlusion portal culling detection algorithm. This

simpliﬁed version discards all zones that have a low

probability of being seen and also disregards orienta-

tion and assumes that the observer can see simultane-

ously north, south, east and west directions. For each

ICE-B 2008 - International Conference on e-Business

of these directions the observer throws a vision cone

having its center coincident with the direction at hand.

The ﬁrst intersected walls are considered to be vis-

ible, and the others invisible. The Figure 4 summa-

rizes with descriptive colors which were the shelves

that were most observed by the moving targets.

Figure 4: Visible Walls with Historical Data.

4 RESULTS

In this section, the project’s results will be de-

picted taking into account three generic levels: sys-

tem’s main features; simulation statistics extraction

and global aplication; and architecture stability and

feasability.

For simulation purposes, there had been consid-

ered two standard computer conﬁgurations: a high-

end machine with 4GB of RAM, an Intel Core 2 Duo

E820 CPU and a SATA II 320 GB 16 MB cache hard

disc denominated as conﬁguration A, and a low-end

conﬁguration, denominated as B, equiped with a 2 GB

of RAM, an Intel Pentium D 3.00GHz and a SATA II

250 GB 8MB cache hard disc. Both systems were

equiped with Windows Vista Ultimate and the simu-

lations were performed with similar workload condi-

tions. In Figure 5 the experiment’s results are fully

depicted as for both hardware conﬁgurations four dif-

ferent scenarios were simulated. For each one it was

recorded the CPU time needed to perform the most

demanding task – real-time dynamic grid concentra-

tion levels with memory-based historical data – and

the presence of absence of image ﬂicker, with a dif-

ferent number of tracked items ranging from a single

one to one thousand.

As previous note, one shall point that for single

item tracking, the measured CPU time for both con-

ﬁgurations is not available as the benchmarking tool

reported zero seconds. The results showed that for ten

items, the differences between low-end and high-end

computers is absolutely negligible. For one hundred

items, conﬁguration B needs twice the time of con-

ﬁguration A but real-time visualization is not jeop-

erdized in anyway. In both cases, for this scale there

was not registered any ﬂicker effect and the process

time was compatible with a real-time system. Only if

the scale is pushed to one thousand, conﬁguration B

takes three point seven seconds to compute and even

conﬁguration A takes two point three seconds. These

ﬁgures show that for this kind of scale it is needed

a high-end computer system – even if one consider

the traditional consumer market products – and hard

real-time requirements are not met but one might still

assume near real-time features that are perfectly ad-

equated for this kind of management/monitoring sys-

tems.

ConfigurationA ConfigurationB

CPUTime(ms) N/A N/A

Flicker N N

CPUTime(ms) 15 23

Flicker N N

CPUTime(ms) 125 215

Flicker N N

CPUTime(ms) 2300 3700

Flicker Y Y

NumberofItems

100

1000

Figure 5: Simulation Performance Benchmarking.

Regarding the ﬁrst aspect, all the enunciated pre-

dicted functionalities, thecnically described in the

previuos section, were successfully implemented and

fully tested. As illustrated in Figure 6, it is possi-

ble to visualise in real-time the location of up to one

thousand items overlapped with the shop ﬂoor layout.

This number of items can be increased but it is de-

pendent of the external location system’s features. In

the same illustration, it is visible the tool’s ﬂexibility

in what concerns to layout management and design

as all shop ﬂoor static structures are fully deﬁned and

described through a simple, yet ﬂexible XML conﬁg-

uration ﬁle. With this approach, it is possible to model

heterogeneous environments and, therefore, apply the

proposed system to several domains.

In spite of the importance of the mentioned func-

tionalities, the greated added value resides in the

knowledge extraction extendend features. Having in

consideration the online item location gathering, the

system is able of real-time item path reconstruction

and visualization operating both in memmory-based

or database access, depending on data dimension.

Conducted experiments showed that real-time mem-

mory access is feasible using a low-end computer –

with 2 GB of RAM – for tracking one hundred items

for a period of an hour at a medium pace.

BUSINESS INTELLIGENCE THROUGH REAL-TIME TRACKING - Using a Location System Towards Behaviour

Pattern Extraction

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Figure 6: Real-Time Item Location.

As illustrated in Figure 7 the location data is used to

extract more signiﬁcant information about item dis-

persion/concentration both in the present and also

considering historical data. It was used a gradient

scale where concentration levels vary through the

RGB scale where red means high levels of concen-

tration and blue low levels.

Figure 7: Historical Concentration Matrix.

One signiﬁcant functionality of this model is the pos-

sibility to perform calculations based on completely

ﬂexible and dynamic projection grid. This option

proved to be efﬁcient on online data processing for

a signiﬁcant number of tracked items - approximately

one hundred - without database access by using a tem-

poral location matrix. This feature enables a full de-

tailed concentration analysis in real time when recur-

ring to a high deﬁnition grid that divides the layout in

small areas; and enables swift high big-picture studies

when using a less tight net. This capability is appro-

priately described in Figure 8.

Finally, considering the features results description,

one shall paint the relevance of the vision module.

Figure 8: Dynamic Concentration Grid Example.

This application requirement performs the emulation

of an omnidirectional vision of each trackable item.

The described algorithm is able of identifying the

visible objects, having into consideration both single

instantaneous data and historical information, previ-

ously collected and stored – in direct memmory ac-

cess or in a database. In the conducted experiments,

this system’s module also showed high levels of efﬁ-

ciency and correctness; much similar to the ones al-

ready described in the above paragraph.

Regarding the system’s global architecture def-

inition and implementation, the undertaken simula-

tions demonstrated its adaptative capability through

its ﬂexibility in what concerns to both the database

provider and, perhaps more important, to the loca-

tion system tecnology. These caracteristics greatly

enhance the whole system’s applicability in several

scenarios. Still in this domain, the distribuited sys-

tem’s design enables the usage of low-end computers.

Therefore it constitutes an incentive to client’s IT in-

frastructure reusage while minimizing the solution’s

economical impact. Simultaneously, this approach

enables greater site manager’s empowerment through

real-time information access to all system’s features

visualization. These actions can be triggered for both

partial and global organization providing more and

deeper analysis points of view.

5 CONCLUSIONS

Considering the project’s simulation environment and

the achieved results, one shall state that, although the

location engine had been implemented in order to re-

alistically simulate traceable items, all concept has

been demonstrated. The developed prototype proved

to be efﬁcient and effective in large scale distributed

data gathering and real-time item location visualiza-

tion.

Taking into account the system’s architecture, it

was veriﬁed the concern in allowing multi-store man-

agement with both distribuited modules and central

integration concerns that enable consistent and online

knowledge extraction and visualization. Having in

mind the different application modules, one ought to

refer that the integration with the XML-based layout

manager proved to be extremely ﬂexible to accom-

ICE-B 2008 - International Conference on e-Business

modate distinct real scenarios and yet realizable and

realistic.

Considering the most noble project’s slice, one

ought refer to the previously enunciated knowledge

extraction features. Having as support the results de-

picted in the last section, it is secure to state that the

system is able to produce real-time shop-ﬂoor visual-

ization maps with intelligible data on online item lo-

calization; individual item complete path routes; on-

line and historical population density rates and path

routes concentration; and also item-vision enabled

concentration maps – as emulation for item omnidi-

rectional vision, yet considering occlusions. All of

these features are allowed for graphical user interface

through different grid dimensions for distinct analy-

sis granularity. Bearing in mind the project as whole,

one shall state that the developed knowledge extrac-

tion platform with online and diversiﬁed visualization

tools constitutes a solid ground for online item track-

ing and heterogeneous space management with dis-

tributed capabilities. One ﬁnal major advantage of the

proposed system resides in the total independence re-

garding the external position engine both in terms of

suppliers and even more important in terms of base

technology.

In spite of the enunciated project’s accomplish-

ment, an even by being in prototype stage – yet re-

liable and fully functional – there are several future

work areas that are able to greatly enhance the sys-

tem’s appliance and success. From these, the most

relevant ones are believed to be centered in eccen-

tric shop-ﬂoor layouts both in terms of shape and

multi-level buildings; complete path routes analysis

enabling common node fusion for global paths proba-

bilistic construction; ﬂexible and dynamic report def-

inition tool with conﬁgurable alarm triggering; and

perhaps the most interesting would be the character-

ization of ’what-if’ scenarios with simulated trafﬁc

based on real historical data. Considering both the

project current achievements and the depicted future

work areas, one might identify the most desirable im-

plementation domains. Although there are not limited

to, the proposed system might be useful for traditional

retail environment for shopping cart tracking; tracing

clients through a commercial area such as shopping

centers; enabling item tracking and route analysis in

an hospital; producing activity reports and analysis in

controlled areas such as penal complexes, mental in-

stitutions or closed educational organizations.

ACKNOWLEDGEMENTS

The authors would like to thank Professor Eugnio

Oliveira for inspiration and the contribution regard-

ing behavior pattern recognition and artiﬁcial intel-

ligence guidelines, Professor Augusto Sousa for en-

lightenment in the portal cells algorithms.

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BUSINESS INTELLIGENCE THROUGH REAL-TIME TRACKING - Using a Location System Towards Behaviour

Pattern Extraction