=Paper=
{{Paper
|id=Vol-461/paper-3
|storemode=property
|title=A Hybrid Storage Model for Web Information Systems
|pdfUrl=https://ceur-ws.org/Vol-461/paper3.pdf
|volume=Vol-461
}}
==A Hybrid Storage Model for Web Information Systems==
https://ceur-ws.org/Vol-461/paper3.pdf
A Hybrid Storage Model for Web Information
Systems?
Mark Roantree
Interoperable Systems Group, Dublin City University,
Glasnevin, Dublin 9, Ireland
mark@computing.dcu.ie
Abstract. It is often a requirement of Web Information systems that
they incorporate data from multiple and heterogeneous data sources.
By definition, this means that data for the same object may be spread
across different databases and be stored in different formats. In sport
science applications, this problem is exacerbated by the fact that data
generated through experiments or sensor devices may lack normalisation
and synchronisation. The application of a DataSpace architecture and
processes provides a landscape in which solutions to these problems can
be built. In this paper, we discuss a real-world sport science applica-
tion where data is continually recorded and stored in different databases
types. The different layers in the DataSpace architecture together with
the cross-cutting services are exposed to illustrate how these issues are
resolved.
Keywords: DataSpace, Web applications, multi-model storage, metadata
1 Introduction
The HealthSense project is a collaboration between the Interoperable Systems
Group and the School of Health and Human Performance at Dublin City Uni-
versity in Ireland. The broad aim of the collaboration is the management of all
data generated from healthcare laboratory and field tests, with special empha-
sis on the performance and welfare of high performance athletes. Both research
activities and engineering decisions are driven by the needs of the health spe-
cialists who provide end user testing and feedback. This paper focuses on the
architecture and services specified and built for the Web Information System
currently in use. Specifically, we focus on the heterogeneous information sources
used to capture scientific data, and briefly discuss the views that integrate these
systems.
Previously, the evaluation of physiological responses during both competition
and exercise was limited to a controlled laboratory environment, an approach
that lacked ecological validity. It was recognised that a more holistic approach,
integrated data or evidence from multiple sources was required to improve deci-
sion making. Integrating multiple sources of data can be used:
?
Funded by Enterprise Ireland Grant No. CFTD/07/201
Proceedings of WISM 2009
– To view the average heart rate at specified intervals during exercise or com-
petitive games. This is currently a challenge as the synchronisation of team
based data is often difficult [1] and sensor data requires integration with
player/patient demographics to include a player’s maximal heart rate.
– To compare heart rate responses over historical data or across a number of
games played in different conditions.
– To determine the effect of environmental conditions on heart rate responses.
– To measure how changes in the size or dimensions of the playing surface
and/or the number of players per team can alter the physiological load (de-
termined by heart rate) [2].
– to determine the period of time that individual players exercise above or
below a predefined heart rate, or percentage of their maximal attainable
heart rate (heart rate max).
– To combine this information with laboratory based data to determine the
percentage of maximal aerobic capacity (VO2max), and the percentage heart
rate corresponding to the ventilatory and/or lactate threshold.
The above requirements represent offline analysis of data generated from a
number of sensor streams and in some cases, require innovative processes to pro-
vide solutions. A further problem arises when the specialist requires immediate
or live querying of data as it is generated from sensor devices. Providing real-
time query responses to coaches as to when a player reaches a pre-determined
excessive level of physiological response (e.g. heart rate), either momentarily or
for a pre-defined extended time period, has been shown to optimise a player’s
performance [3]. The coach may also wish to integrate corresponding video and
movement (velocity) data to identify the tasks the athlete was undertaking to
determine if it was an appropriate physiological response. This information could
be used by the coach to design and implement an appropriate intervention. For
example, an immediate response may be to remove the player from the current
activity, or change the tactic or drill. The ability to query and respond to the in-
formation in real-time will allow teams and individual athletes to gain feedback
from experts immediately. Furthermore, by providing this functionality in the
form of web-based systems, it often eliminates the need to travel to the training
site.
1.1 Contribution and Structure
The motivation for this research project is to provide a data infrastructure and
query management system for the diverse requirements of sport scientists in
a scalable sensor network environment. A principle challenge arises from the
fact that we are dealing with real world requirements, that take their data from
multiple sources, and that much of this data is in a raw and unstructured format.
This presents the first problem: queries are not possible without building low
level software to generate predefined queries. This solution has little innovation
and is not practical for users. Further challenges arise as queries across multiple
players and teams require that data must be synchronised across all players
Proceedings of WISM 2009
to ensure that players are measured using the same criteria. Furthermore, by
providing one set of solutions to the above problems, we create new problems.
For example, the use of XML to enrich raw sensor data introduces the problem
of query performance with large XML caches. Finally, the modern landscape
for personal health systems, especially for high performance athletes, requires
an approach to manage the data everywhere scenario that now presides. The
distributed or federated database architectures will not suffice here as the levels
of autonomy and degrees of heterogeneity across systems are too great.
Thus, the goal of this project (the focus of this paper) is to provide an
architecture that is flexible enough to accommodate the user’s hybrid storage
requirements; incorporate the services required to provide base functionality of
queries, integration, updates; and finally to provide autonomy for key systems
and certain research and development work packages, while on the other hand,
providing integration solutions for processes running over multiple data sources.
The adoption of a web-based information model provided the standard interface
(across multiple platforms) but a novel method for managing and analysing data
sources was required. The concept of the DataSpace system was introduced in [4]
and [5] as a solution to organisations such as healthcare or sport scientists who
have a requirement for ”large numbers of diverse but interrelated data sources”.
While the adoption of this architecture has been slow (see Sect. 4), we adopt the
basic principle and extend it to provide more detail of how it can be effective in
managing data from heterogeneous and non-traditional data sources.
The contribution of this paper is the application of the DataSpace architec-
ture to a personal health sensor network. Unlike other sensor network appli-
cations, this research provides a holistic approach by exploiting the DataSpace
architecture to incorporate data from multiple sources and thus, providing a
meaningful query engine on raw sensor data. By incorporating data from demo-
graphic databases, it is possible to run detailed analysis on sensor streams on
both real-time data streams and offline databases. While the main focus of this
work is on describing the WIS and Dataspace architectures, we also provide a
brief demonstration of our work in combining multiple sensor streams through
the functionality of our Metadata Service.
The paper is structured as follows: in Sect. 2, we describe the DataSpace
architecture as it provides for a pHealth Sensor Network; in Sect. 3, we describe
a sample query with some of the times recorded; while in Sect. 4 we discuss
similar approaches; and in Sect. 5, we provide an overall summary.
2 Sensor Web Architecture
In this section we describe the major components of the HealthSense DataSpace
system and how each component contributes to the information management
process.
Proceedings of WISM 2009
DSMS
Application Web Portals
Data entry – KW queries – Lab/Sensor entry
System Monitor
Optimisation
Multi-language
GUI, graphs
XML Query
Enrichment
Scalability
& Metadata
Searching
Semantic
Service
Profile
raw data Injury DB ESJ (Oracle)
System
Storage Repository
XML sensors video/image db
data
Fig. 1. SportSense DataSpace Architecture
2.1 Storage Component
The HealthSense Storage Component comprises both data sources and a metabase
that is used for understanding the content and semantics of the actual data
sources. The key difference between the DataSpace architecture and more tra-
ditional distributed architectures is that the data is subject oriented, similar to
a Data Warehouse. In this DataSpace, sensor data exists in both raw format
(binary or textual files) and in an enriched XML format. The raw format is
necessary for live queries as the converted files are not available quickly enough
for this purpose [6]. An Object-relational model (Oracle 10g) is used for creating
subject (athlete or patient) profiles and is also used for managing injury data. A
separate system is used to store video data while the video metadata is stored in
a relational database. Unlike federated database systems that are created from
existing application databases, this is a green-field architecture with some level
of control to make integration of data easier. However, there will always be sit-
uations where unplanned integration will be necessary. For example, combining
the output from new sensor devices with previously generated data.
DataSpace Repository The repository for the HealthSense DataSpace pro-
vides the engine for the DataSpace system and has a complex metamodel. As
with the DataSpace System itself, the System Repository (or metabase) also
adopts a hybrid storage model structure. This is necessary as some of the con-
structs involved are not suited to traditional storage systems. While a full de-
scription of the repository and its Metadata Service form part of a separate body
of work [6], we provide a brief description of the major components now.
Proceedings of WISM 2009
– Integrations. Relationships across separate data sources with semantics for
integration.
– Profiles. For each user or user type, a profile is created that links the user
to specified data sources. It may provide a link to Integration objects where
users are managing data from multiple sources.
– Templates. Templates are used to describe raw sensor sources. Together
with a structural enrichment process, they form XML schemas and are used
to populate these schemas with raw data so that they can be queried using
XPath or XQuery [1].
– Contexts. While template objects provide for the creation of XML data
from raw sensor output, this provides only a structural enrichment of the
sensor data. With Context objects, it is possible to semantically enrich the
file. Contexts provide the necessary background to understand the situation
in which each sensor was used. For example, a Heart Rate monitor can be
used in a match situation: football, tennis or athletics; or it may be used in
testing scenarios such as the Bangsbo test [7].
– Schemas. Schema objects are stored for XML databases only. They provide
the user with a storage model version of raw sensor data and enable the user
to formulate queries. There is a strict one-to-one mapping between templates
and schemas.
– Replicas. There are many examples of data replication in the DataSpace
system and these are modelled in the system repository. For example, the
optimiser will create a relational index of an XML database; multimedia
metadata is created for video files; XML views are created from object-
relational databases for the purpose of sharing data.
2.2 Profile Component
HealthSense is a Web Information System in that web browsers provide the
interface to multiple sources of data, and HTML or XML is used as an interface
between heterogeneous data collections and users. What connects user types
with different requirements, to one or more data sources are the profiles. Many
profiles are simple to construct: a physiotherapist will only query and manage
data from a single database (Injury DB) or a knowledge worker who wants to
average team heart rate data after each match. However, some of the profiles
are more complex: knowledge workers wishing to mine larger data volumes are
searching for relationships between training regimes (Electronic Sports journal),
maximal heart rate and injuries sustained.
2.3 Service Component
All of the Service Components represent research and development projects. For
the HealthSense DataSpace, the XML Optimisation Service has been created
but continues to form ongoing research [8]. The Semantic Enrichment service has
been completed for offline sensor analysis [9, 1] but is part of current research
for querying live streams.
Proceedings of WISM 2009
Query Service. There are two forms of standard query interface: XQuery and
a hybrid of XPath and SQL, both accessible through standard web browsers. Un-
like all of the existing sensor networks we have encountered, we provide database-
style query functionality. This is achieved by the Semantic Enrichment service
that delivers XML versions of all sensor data streams. Together with the Meta-
data Service, a sensor stream is attached to an individual who is described in
the Electronic Sports Journal (ESJ) database as seen in figure 1. In other words,
as soon as new sensor data is received, it is ready for processing within a short
period of time. The enrichment effort is described in [9], together with the times
required to both structurally edit and semantically enrich sensor data sources
(64MB in less than 1 minute).
Semantic Enrichment. This service creates the XML version for all sensor
streams using a template approach [9] that requires no modification to sys-
tem components when new sensors are introduced. Initially, raw sensor streams
are structurally enhanced to produce basic XML files and subsequently, these
files are mined to generate additional semantics for every sensor reading. In all
sporting experiments, we apply state information to each sensor reading, where
a state refers to some interval in either a sporting event (eg. football game or
tennis match) or a lab-based training activity.
2.4 Application Component
The application layer is deliberately kept simple and serves as a portal to under-
lying data sources. Thin web browser applications are used to query, run update
operations (for example recalibrate sensor readings), run analytical functions
and view video data.
3 Case Study and Experiments
On an ongoing basis, data is collected from a series of experiments conducted
on teams playing Gaelic (Irish) football, practice tennis matches, and laboratory
tests. The application area for this research is typical of a heterogeneous data
environment and well-suited to a DataSpace architectural model with sensor data
collected both wirelessly and through USB connections. Some of the existing
sensors are now described:
– Polar S625XTM heart-rate monitor. This consists of a fabric band which
fits around a person’s chest and generates heart rate data every 5 seconds. It
also includes a foot pod that accurately captures velocity and the distance
covered.
– BodyMedia SenseWear R . This sensor array is worn around the upper
arm and measures and logs the following: galvanic skin response, a measure of
skin conductivity which is affected by perspiration; skin temperature, which is
linearly reflective of the body’s core temperature activities; heat flux which
Proceedings of WISM 2009
is the rate of heat being dissipated by the body; subject motion using an
in-built accelerometer. Data values are generated every 60 seconds.
– iPod Nano 4G with Nike R + IPod Sport kit. This sensor includes
a foot pod and a detector connected to the IPod Nano. Distance covered
during a walk or run together with caloric consumption is recorded.
3.1 Data Extraction
The application layer contains a series of web-based applications for player di-
ary, sensor, video and injury data. The sensor data is uploaded by players for
individual data, and by technicians for team-based or laboratory-based data. In
figure 1, sensor uploads will impact the raw data and XML sensor databases,
together with the relational index for all XML schemas.
Example 1. BodyMedia Data
1
1
1195226100000
30.126686096191406
1.4301998615264893
A sample BodyMedia enriched to XML format is illustrated in Example
1. Each sensor device has a small XML template file associated with it. The
enrichment processor combines the template and output from the device to create
an XML schema that can be queried using the XPath query language. If the
system encounters a new sensor, all that is required is its template. The template
describes the sensor’s output in terms of its structure and the location of key
elements, such as start time and measurements, as well as important data such as
value delimiters. The output file contains user, session and device ID information,
followed by sensor-specific information and finally, a list of labelled measurement
values. A more complete discussion on this process is provided in earlier work
[9].
3.2 Data Enrichment and Integration
Any number of sensor streams may be merged in advance of user queries. As
sensor data is not as complex as database schemas, the integration process is
not at the same level of difficulty as in federated or mediated schemas. However,
this is replaced with issues such as normalisation of data and synchronisation
across teams and multiple experiments [1]. The DataSpace Repository nominates
various attributes that are used as axis points for integration eg. time as shown
in Example 2, where data is merged from heart rate and BodyMedia sensors.
Proceedings of WISM 2009
Example 2. Integrated Data
1
1
91
30.126686096191406
1.4301998615264893
95
97
The queries shown in Table 1 are examples of simple XPath queries that can
be used when data is required purely from XML sources. In many queries, it
has been necessary to construct a hybrid of XPath,SQL and Java to extract the
information described in the introduction to this paper. Space issues prevent a
deeper discussion of how these queries are constructed but the purpose of this
section is to demonstrate how raw sensor data can be queried in the existing
DataSpace architecture.
XPath Expression Query
//user[id=“Sarah”]//measurement Return all sensor readings for Sarah when
[skin temp average original rate [text() >30]] her skin temperature is higher than 30
//user[id=“Fionnula”]//measurement Return sensor readings for Fionnula when
[HRValue[text() >120]] the heart rate exceeds 120
//user[id=“Aoife”]//measurement Return sensor readings for Aoife where
[skin temp average original rate[text() >30] the skin temperature exceeds 30 degrees
and HRValue[text() >130]] and heart rate exceeds 130
Table 1. XPath Query Examples
4 Related Approaches
Although DataSpace systems were first introduced in 2005, research is this area
is not widespread and in many cases, it is used to address the financial side of
information systems where access to information and services requires payment.
While we do not currently address the pricing aspect of information access, other
Proceedings of WISM 2009
characteristics of these systems are common between our approach and that of
others.
Research work that predates the DataSpace approach but is very similar
in requirement to our work can be found in [10]. In this work, the authors
also claim to provide an automated integration into the scientific database, and
their architecture has a similar structure to HealthSense. While they employ
a method of algorithmic analysis to process incoming data on an automatic
basis, we employ a template-based process to ensure that all data streams are
properly processed and transformed. Thus, we can greatly reduce the number
of errors when importing data. Any issues to do with integration are deferred
until query processing. The other major difference is that they employ relational
database technology for data storage while we do not believe that this approach
can adequately manage the heterogeneous data types that are generated in the
sports science domain.
In [11], the authors describe a problem domain similar to the HealthSense
project in that users require access to, and management of data located in both
structured and unstructured sources. This work provides more detail than we can
provide in this paper on the hybrid query language required for heterogeneous
data sources. The work is heavily focused on a keyword approach to data retrieval
only. In [12], the same research group describe a process for managing uncertainty
across multiple relational databases. While this provides a significant advance
on integrating data sources with uncertainty and is focused on the DataSpace
environment, it does not address the larger issues of DataSpace Management,
the required services, or the continuous importation of sensor data in raw format.
In [13], the authors present a Personal DataSpace Management System called
iMeMex. This is similar to our work in that it provides a holistic approach to
data management: files, emails, pictures, videos and calendar are all part of
the system’s repository. They employ a DataSpace platform and also similar
to our approach, they provide an operational prototype. However, their focus
is limited to data for a single individual whereas we are required to manage
data for multiple users from heterogeneous data sources and to manage the data
required by sensor devices.
5 Conclusions
In this paper, we describe a collaboration between a data engineering research
team and a group of well established sports scientists working with both elite
sports men and women, and members of the general public undergoing health
trials. As is typical with many scientific data collections, it was quickly identi-
fied that a single point of storage, with a single data model would not provide
a solution to their needs. A multi-storage system with standard access facilities
emerged as key requirements. The adoption of a web-based information model
provided the standard interface (across multiple platforms) but a novel method
for managing and analysing data sources was required. The HealthSense Sys-
tem is a fully operational prototype that works with real-world data and as a
Proceedings of WISM 2009
characteristic of a DataSpace architecture, it permits ongoing research into some
services while users can still import sensor data and run daily queries.
Our current efforts are focused on optimisation of both database and stream-
ing data. On the database side, we are continually optimising both read and up-
date operations on XML databases, while also building a query service across all
hybrid data sources. For querying live sensor data (during match time or training
activities), XML streaming approaches cannot be adopted as the time required
to convert raw data to XML is not acceptable to health specialists. Thus, a hy-
brid streaming approach has been developed [6] to use schema information from
the metabase to map XPath queries to lower level querying constructs.
References
1. Roantree, M., McCann, D., Moyna, N.: Integrating Sensor streams in pHealth Net-
works. In: 14th International Conference on Parallel and Distributed Systems, pp.
320-327, IEEE Computer Society Press (2008)
2. Hill-Haas, S., Coutts, A., Rowsell, G., Dawson, B.: Variability of Acute Physiological
Responses and Performance Profiles of Youth Soccer Players in Small-sided Games.
Journal of Scientific Medical Sport, 11(5):487-90 (2008)
3. Impellizzeri, F.M., Marcora, S.M., Castagna, C, Reilly, T., Sassi, A., Iaia, F.M.,
Rampinini, E.: Physiological and performance effects of generic versus specific aer-
obic training in soccer players. Int. J. Sports Medicine, 27(6):483-92 (2006)
4. Franklin, M., Halevy, A., Maier, D.: From Databases to Dataspaces: A New Ab-
straction for Information Management. SIGMOD Record, 34(4), pp. 27-33 (2005)
5. Halevy, A., Franklin, M., Maier, D.: Principles of Dataspace Systems. In: 25th Sym-
posium on Principles of Database Systems, pp. 1-9. ACM Press (2006)
6. McCann, D., Roantree, M.: A Hybrid Query Service for Optimising Raw Sensor
Data. Submitted for publication (2009)
7. Chamari K. et al.: Field and Laboratory Testing in Young Elite Soccer Players.
British Journal on Sports Medicine, vol. 38, pp. 191-196 (2004)
8. Marks, G., Roantree, M.: Pattern Based Processing of XPath Queries. In: 12th In-
ternational Database Engineering and Applications Symposium, pp. 179-188 (2008)
9. Camous, F., McCann, D., Roantree M.: Capturing Personal Health Data from
Wearable Sensors. In: International Symposium on Applications and the Internet
(SAINT), pp. 153-156. IEEE Computer Society Press (2008)
10. Leser, U., Naumann, F.: (Almost) Hands-Off Information Integration for the
Life Sciences. In: 2nd Innovative Data Systems Research (CIDR), (Online www-
db.cs.wisc.edu/cidr) pp. 131-143 (2005)
11. Liu, J., Dong, X., Halevy, A.: Answering Structured Queries on Unstructured Data.
In: 9th International Workshop on Web and Databases, pp. 25-30. ACM Press (2006)
12. Dong X., Halevy A., Yu C.: Data Integration with Uncertainty. In: ACM 33rd Very
Large Databases, pp. 687-698. ACM Press (2007)
13. Blunschi L. at al.: A Dataspace Odyssey: The iMeMex Personal Dataspace Man-
agement System. In: 3rd Conference on Innovative Data Systems Research (CIDR),
pp. 114-119 (2007)