=Paper=
{{Paper
|id=Vol-1630/paper4
|storemode=property
|title=Contextual Data Collection For Smart Cities
|pdfUrl=https://ceur-ws.org/Vol-1630/paper4.pdf
|volume=Vol-1630
|authors=Henrique Santos,Vasco Furtado,Paulo Pinheiro,Deborah L. McGuinness
|dblpUrl=https://dblp.org/rec/conf/semweb/0002FPM15
}}
==Contextual Data Collection For Smart Cities==
https://ceur-ws.org/Vol-1630/paper4.pdf
Contextual Data Collection for Smart Cities
Henrique Santos1,2 , Vasco Furtado2,3 , Paulo Pinheiro1 , and
Deborah L. McGuinness1
1
Rensselaer Polytechnic Institute, Troy, NY, U.S.A.
2
Universidade de Fortaleza, Fortaleza, CE, Brazil
3
Fundação de Ciência, Tecnologia e Inovação de Fortaleza, Fortaleza, CE, Brazil
Abstract. As part of Smart Cities initiatives, national, regional and lo-
cal governments all over the globe are under the mandate of being more
open regarding how they share their data. Under this mandate, many of
these governments are publishing data under the umbrella of open gov-
ernment data, which includes measurement data from city-wide sensor
networks. Furthermore, many of these data are published in so-called
data portals as documents that may be spreadsheets, comma-separated
value (CSV) data files, or plain documents in PDF or Word documents.
The sharing of these documents may be a convenient way for the data
provider to convey and publish data but it is not the ideal way for data
consumers to reuse the data. For example, the problems of reusing the
data may range from difficulty opening a document that is provided in
any format that is not plain text, to the actual problem of understand-
ing the meaning of each piece of knowledge inside of the document. Our
proposal tackles those challenges by identifying metadata that has been
regarded to be relevant for measurement data and providing a schema
for this metadata. We further leverage the Human-Aware Sensor Net-
work Ontology (HASNetO) to build an architecture for data collected in
urban environments. We discuss the use of HASNetO and the supporting
infrastructure to manage both data and metadata in support of the City
of Fortaleza, a large metropolitan area in Brazil.
Keywords: smart cities; sensor network; data quality
1 Introduction
Smart Cities should be sustainable, safe, inclusive, walkable, creative and in-
novative. In one way or another, the availability of each one of these features
characterize a desirable place to live. Obtaining any of them, however, requires
two key components: access to and understanding of city data. Consequently, a
city’s ability to produce and share relevant data that can be understood is crit-
ical and can be viewed as key indicator of a Smart City. Further a city’s ability
to derive knowledge from this data and further, to use it to power innovation is
an even better indicator of a smart city.
Two complementary trends are particularly relevant in this context: the Inter-
net of Things (IoT) and the Open Government Data approach (OGD). The IoT
2 Henrique Santos et al.
is already a reality in many large metropolitan areas around the world. A myr-
iad of sensors operating at different levels of autonomy are deployed throughout
cities collecting data from every aspect of these cities’ rich urban environments.
Governments are increasingly sharing their data, often with the goal of promot-
ing innovation via societal participation with the use of the data. In the context
of data sharing, different categories of stakeholders may be identified: designers
and software developers may use data to produce public services through the
use of web and mobile applications; scientists may produce elaborate analyses
and studies about the cities; public officers may use the data to improve city ad-
ministration through the use of effective data-based decision-making techniques;
journalists may use open data to produce more reliable, factually-based and at-
tractive news. Briefly, IoT and OGD are enabling knowledge production that is
fundamental for enhancing city “smartness”. While foundational elements exist
with IoT and OGD, many challenges remain to truly realize the potential of
widespread knowledge generation from the raw data sources increasingly avail-
able in many cities.
One key challenge that we address in this paper is the need for cities to
provide metadata that enables data consumers to understand publicly shared
data, including the context within which data was collected. The lack of proper
metadata significantly impacts the extraction of knowledge from data for any of
stakeholders identified above. The reliability of data collected by extensive sensor
networks, for example, relies heavily on knowledge about sensor deployment,
sensor calibration, measurement units, measurement accuracy, and many other
types of knowledge that is often lost at measurement time.
Open government data (which includes monitored data from city sensors) is
typically published in data portals that provide access to the contents of datasets.
It is true that datasets are a convenient way to convey and publish data but,
while that holds true on a technological perspective, they are not always the best
suited for final users to use as the boundaries of a dataset may be more driven
by technical considerations of sensor collection rather than boundaries more
natural for users. Further the context for the boundaries is often not captured
or communicated. The datasets are merely enclosures for data, which may be
serialized inside them in a number of formats using different domain vocabularies.
The datasets hopefully come with metadata that attempt to explain the formats
used to serialize the data and also to give meaning to the domain vocabularies
used. Accurately interpreting the contents of the data in one data portal even
with a metadata file is often a challenge and greater challenges exist when users
attempt to integrate data from multiple portals.
Our work addresses the lack of a vocabulary to describe the knowledge about
sensor network measurements in the context of Smart Cities. We introduce the
Human-Aware Sensor Network Ontology for Smart Cities (HASNetO-SC) used
to describe knowledge associated with the collection of city empirical data, focus-
ing in particular on the collection of city empirical data coming from city-wide
sensor networks. The rest of the paper is organized as it follows: In Section
2, we discuss work related to the handling of urban stream data and to the
Contextual Data Collection for Smart Cities 3
development of ontologies describing empirical data collection. In Section 3, we
present and discuss our research choices regarding HASNetO-SC and the process
of collecting urban data. In Section 4, we describe the use of a HASNetO-SC-
based tool when applied to a network of sensors deployed throughout a large
metropolitan area. In Section 5, we discuss our findings and future work.
2 Related work
2.1 Open Data Portals
Open government data initiatives around the world are often very large projects
in terms of complexity and data volume [9] [17]. Goals behind these initiatives
range from government transparency to fostering societal engagement in govern-
ment decisions. Typically open government data efforts for disseminating data
are centered around the development and publication of online data portals. For
example, many of these portals are built on top of CKAN4 , which operates as a
comprehensive dataset repository with data coming from a plethora of distinct
government agencies. Many times, CKAN-hosted datasets convey data gathered
empirically by city sensors. The data often are consolidated into datasets be-
cause of technology restrictions. This approach, although convenient for data
producers, is often a hindrance for data consumers.
Much work exists on the integration and publication of urban data collec-
tions. QuerioCity [11] describes a Linked Data platform to publish, search and
link city data from static datasets or stream data coming from deployed sensors.
This platform is able to create a semantic catalog that describes the content of
the datasets that reuses standard vocabularies to improve interoperability and
discoverability. AECIS [6] describes an automated discovery and integration sys-
tem for urban data streams. Both works, although capable of dealing with urban
data streams, are not concerned with data quality, in terms of comprehensive
metadata and the sensor network and context where the data was collected.
2.2 Semantic sensor network and Observational data
The concept of Observational data is treated in the literature [15] [18] [14] as
data that is obtained while sensing some property of an entity from the real
world. The result of an observation is a value for that property [20]. City-wide
sensors are also performing observations, making content annotation crucial.
Annotations enable interoperability and discoverability, making data easier to
be understood and thus (re)used. To leverage the potential benefits of data reuse,
several approaches exist to model the infrastructure that generates the data and
to describe data content and context.
Semantic Sensor Networks make use of the description of instruments and
detectors (many times referred to as sensors in the literature) to leverage and
4
http://ckan.org
4 Henrique Santos et al.
maintain complex networks of sensors, while providing integration of the col-
lected data. In [2], twelve different sensor network ontologies are studied and
compared. This work preceded the W3C’s Semantic Sensor Network Ontology
(SSN) [1]. SSN is an ontology that aims to describe sensors, observations and
its related concepts, like sensor capabilities, measurement processes and deploy-
ments. SSN is able to annotate data in a manner that makes it possible to to tell
if it is coming from a certain sensor, using some measurement process to mea-
sure a certain property of an entity of interest. BOnSAI [19] and the SESAME
Meter Data Ontology (SMDO) [4] are other sensor network ontologies that are
focused on smart buildings. Although they are all able to describe the sensor
network behind the collected data, SSN does not rely on standard provenance
approaches, like the W3C’s PROV-O. Besides that, SSN is an ontology, and is
not concerned about how the data is conveyed from its collection in terms of
format and datasets. BOnSAI and SMDO are not scientific-centric ontologies,
not containing key concepts like scientific activities.
O&M [3] is an XML implementation from the Open Geospatial Consor-
tium(OGC) that defines a schema for modeling observations and their results.
In [10], an observation and measurement ontology is proposed that makes use of
O&M’s definitions. OBOE (The Extensible Observation Ontology) [12] is an on-
tology for ecological observational data. It provides a data model that captures
measurement semantics and that can be used to streamline data integration. To
achieve this goal, the OBOE ontology contains concepts and relationships for
describing observational datasets.
The Human-Aware Sensor Network Ontology (HASNetO) [13] is an ontology
for describing the scientific activities involved with the data collection of observa-
tional data, i.e., data that is collected while sensing the environment. HASNetO
reuses three ontologies to achieve this task: W3C’s PROV-O, the Virtual Solar-
Terrestrial Observatory - Instrument module (VSTOI) [5] and OBOE. By using
PROV-O, HASNetO is capable of asserting the provenance of the measured data
using PROV terms: activities, entities and agents. HASNetO links VSTOI and
OBOE concepts to PROV concepts: (i) vstoi:Deployment and oboe:Observation
are subclasses of prov:Activity; (ii) vstoi:Dataset is a prov:Entity and (iii) vs-
toi:Instrument becomes a prov:Agent. By doing this, HASNetO enables prove-
nance tracking of data collection activities using W3C’s recommended standard.
Collected data can be encoded in many distinct formats including CSV, XML,
and NetCDF [16]. In many cases, CSV is a format of choice because of its ease
of use by either computers or people. People often manually enter collected data
in a spreadsheet application (like MS Excel or LibreOffice Calc). Spreadsheets
are also capable of exporting content in CSV format. Basically, the CSV format
can be seen as a minimalist enabling approach for data interoperability.
Regardless of the format, no single encoding provides effective mechanisms
for annotating observational data in a way that supports observation as a contex-
tualized measurement collection. For instance, CSV lacks features for expressing
the semantics associated with the data contained in it, so it is challenging to
know, in an automated and interoperable way, the meaning of the data enclosed
Contextual Data Collection for Smart Cities 5
inside a CSV file. For example, it can be difficult to determine if two entries are
observationally equivalent (measured under the same conditions, using the same
units, in the same area, etc.). Nonetheless, different agents may generate data
in different formats and standards, making CSV even more difficult to process
automatically.
Although there are existing approaches for accessing CSV metadata and also
for providing a metadata vocabulary for CSV data, they are typically more con-
cerned with content restrictions, rather than the context in which the CSV data
was collected. W3C’s drafts from the CSV on the Web Working Group5 elabo-
rate on techniques for enabling the access of CSV metadata by describing the
content metadata in a separate JSON file that makes use of RDF vocabulary. To
bridge this gap, we propose Contextualized CSV (CCSV)6 as a format that deals
with both content and context restrictions of the observational data enclosed in
it. The CCSV dataset is basically a regular CSV file with a Turtle preamble
on top of it. The Turtle is used to assert that a certain dataset comes from a
particular instrument during an specific deployment, as context metadata. It is
also used to assert content, by stating the property being measured and which
column contains the measured value.
3 A streamlined process to collect and publish data for
Smart Cities
Our approach relies on the HASNetO ontology. HASNetO was primarily con-
ceived to be used in scientific environments by scientists who conduct observation
and data collection activities on entities of interest, such as a physical, chemical,
biological, social or cultural feature. While comprehensive enough to provide
what scientists need to keep track of their activities, HASNetO faces challenges
when dealing with data collected in large complex settings with broad user bases,
such as urban environments. One of those challenges is that the collected data
must be used not only by the city administration or people involved with their
associated measurements (people who have some knowledge about the data at
some level), but, most importantly, by citizens who have, in general, little to
no knowledge about the collection environment and the collected data. For in-
stance, the accuracy or resolution of an instrument used to collect data may not
be as important for a regular citizen as it is for a scientist. To address these
challenges, we propose a modified version of HASNetO called the Human-Aware
Data Collection ontology for Smart Cities (HASNetO-SC). Our main goal with
HASNetO-SC is to provide smart city stakeholders with a streamlined way of
collecting, preserving and disseminating urban data with an appropriate level of
contextual metadata for data understanding. The following subsections discuss
the aspects of this process.
5
http://www.w3.org/2013/csvw/wiki/Main Page
6
http://tw.rpi.edu/web/project/JeffersonProjectAtLakeGeorge/download/ccsv
6 Henrique Santos et al.
3.1 HASNetO-SC
In 2007, a report on the ranking of the smartest medium-sized European cities
was published [7]. This report aimed to, among other goals, define the general
concept of a Smart City by specifying a set of factors and indicators that a
city should be pursuing in order to increase its level of “smartness”. The work
presented six key areas (characteristics) that were gathered from a literature
search on previous definitions of the term Smart City: smart economy, smart
people, smart governance, smart mobility, smart environment and smart living.
Inside each characteristic, a number of factors were also compiled to support it
and, in turn, each factor consisted of a set of indicators that can identify well a
city is doing with respect to that factor. We developed an extension to HASNetO,
called HASNetO-SC, to describe concepts that exist in urban environments.
HASNetO-SC has been motivated by the smart cities characteristics identified
in the report (not to be mistaken with characteristics of physical entities), while
focusing on the four that had higher potential to produce data that can be
collected empirically and managed using a streamlined process:
– Smart people
– Smart mobility
– Smart environment
– Smart living
The extension basically refines VSTOI concepts that are integrated into HAS-
NetO to better suit urban data collections. The following subsections further
detail our modeling and discuss how we are using HASNetO concepts to cope
with smart cities.
People People are a central component of cities. Cities are made for the citizens,
as the government is intended to foster its society as a whole. Citizens, use city
facilities and participate in the government discussions. In this scenario, people
are capable of providing valuable information to the city. In previous work, [8]
has studied the view of a citizen as a sensor. Our approach is compatible with
this perspective and enables people to either deploy instruments or conduct data
collection activities. By the means of prov:Person, a person is a prov:Agent , as
asserted in the PROV ontology, and the person can have an associated list of
prov:Activity. In HASNetO, prov:Activity is extended to both vstoi:Deployment
and hasneto:DataCollection.
Mobility Mobility is one of the most discussed and explored aspects of smart
cities, due to its need of near-real time raw data (i.e., data from sensors) and
derived data (i.e., results of data analysis). Mobility is central to many key city
goals including accessibility, safe and sustainable transportation etc. To achieve
those goals, many city governments are deploying sensors to monitor a number of
urban mobility indicators. HASNetO enables provenance capture by describing
Contextual Data Collection for Smart Cities 7
how sensors are deployed, in particular how agents perform instrument deploy-
ments at platforms. In VSTOI, a Platform is a surface where Instruments and
Detectors may be attached to measure, in the context above, characteristics of
urban mobility. As a mobility example, we can identify some platforms and in-
struments that are known to be related to urban mobility. We have extended both
vstoi:Platform and vstoi:Instrument to describe these more specialized mobility-
related classes, as seen in Fig. 1. The figure shows that vstoi:Platform includes
three classes: subclasses hasneto-sc:Bus, hasneto-sc:RoadSegment, and hasneto-
sc:LampPost. In a city, we defined buses, roads and lamp posts as being capable
of hosting mobility-related instruments. Furthermore, vstoi:Instrument has been
extended to a series of generic instruments that we propose should be used as a
basis from which to derive specialized instruments, as each city will have distinct
manufacturers, suppliers and vendors of instruments and platforms. The generic
modeling approach described above makes it possible to further connect data and
metadata, making them meaningful to data consumers (e.g., citizens) who are
not necessarily involved with the specification of specialized instruments, even
though they may know consumer instruments including cameras and GPSs.
Fig. 1. HASNetO-SC classes.
Environment Another well-discussed smart city aspect is environment. Ex-
amples of smart environment entities are “air”, “water”, and their associated
monitoring and governance efforts such as “sustainable resource management”,
“pollution control”, and “environmental protection”. Forward-thinking cities are
often concerned with citizen well being and thus are critically interested in envi-
8 Henrique Santos et al.
ronmental aspects. Signals of efficient use of water, electricity, and green space
are some examples of indicators that a city that is doing well in the environment
characteristic. To evaluate those indicators, many instruments are already in
use in big cities that enable the collection of data about air quality, noise levels,
water conditions and so forth. Fig. 1 shows more specialized platforms and in-
struments. Again, those concepts are generic concepts that should be specialized
to fully describe the actual city sensor network.
Living Smart living includes the notions of public health, public safety, edu-
cation and cultural facilities. Smart buildings are becoming a reality with more
sensors being deployed to control access and gather the people flow information.
Public services addressing public health and safety considerations often are plac-
ing sensors including cameras to help monitor potentially dangerous situations.
Fig. 1 also depicts some of the most common platforms and instruments relevant
to smart city living.
3.2 Contextualized Comma Separated Values
As mentioned before, we proposed CCSV as an extension to CSV to address
both content and context restrictions when dealing with observational data. In
our approach, both content and context metadata are needed in order to provide
the connection between data and metadata. From the Smart City perspective,
we needed to align the Turtle preamble of a CCSV dataset with the ontology
concepts introduced by HASNetO-SC. Further in this subsection, the displayed
code is an example of a CCSV preamble of a city dataset.
The preamble contains the following descriptions:
– Knowledge base: In order to provide the possibility of multi-contextual data
collections and also to make the solution more scalable, we make it possible
for the dataset to state which knowledge base should be used for validation
and persistence.
– Deployment: Deployment information makes it possible to link the data the
CCSV dataset conveys to metadata information: (i) instrument and detec-
tor used; (ii) platform and (iii) all attached information to those, including
accuracy, precision, platform location etc.
– Data collection: Data collection is a prov:Activity as asserted in HASNetO.
The use of Data collection information in the CCSV preamble enables the
architecture to have knowledge about this dataset: it is able to know if
distinct datasets were produced under the same context. In other words, the
architecture is able to provide the user enough context information for him
to decide if the data within different datasets can be comparable, joined,
analyzed together, based on his needs.
– Dataset: As previously discussed, datasets are not scientific boundaries for
data, but data collection activities are. In this description, we link datasets
to their corresponding data collections.
Contextual Data Collection for Smart Cities 9
– Measurement(s): Here all measured characteristics are described. For each
measurement type we link its description to a unit and a measured charac-
teristic. We’ll discuss the need for a domain ontology in the next section.
Example of a CCSV Preamble
a c c s v : KnowledgeBase ;
c c s v : hasConnectionURL [ s o m e u r l ] ˆ ˆ xsd : anyURI .
a v s t o i : Deployment ;
prov : startedAtTime [ some timestamp ] ˆ ˆ xsd : dateTime ;
h a s n e t o : h a s D a t a C o l l e c t i o n .
a hasneto : DataCollection ;
a time : I n t e r v a l ;
prov : startedAtTime [ some timestamp ] ˆ ˆ xsd : dateTime .
a v s t o i : Dataset ;
prov : wasGeneratedBy ;
h a s n e t o : hasMeasurementType .
a oboe : Measurement ;
a time : I n s t a n t ;
time : inDateTime ;
c c s v : atColumn 1 ;
oboe : o f C h a r a c t e r i s t i c [ s o m e c h a r a c t e r i s t i c ] ;
oboe : u s e s S t a n d a r d [ s o m e s t a n d a r d ] .
a time : I n s t a n t ;
c c s v : atColumn 0 .
3.3 Domain ontology
An Observation from the scientific point of view is defined as the act of observ-
ing some entity’s property and obtaining a measured value. Measurements are
expressed using a specific standard (or unit). To have measurements properly
annotated, we need a hierarchy of entities of interest with relevant properties,
a hierarchy of entity characteristics with relevant properties, and a hierarchy
of standards to be used in in measurements. The OBOE ontology already pro-
vides a hierarchy of a number of entities, characteristics and measurement units,
10 Henrique Santos et al.
although it is focused on ecological studies. A domain ontology enables deep
linking of the collected data to its metadata. For cities, Section 4 discusses our
initial approach to this ontology in the context of a public transportation system.
4 Use case: Fortaleza urban transportation system
Our primary use cases focus on the city of Fortaleza, Brazil. The city of Fortaleza
is the capital of the Ceará state in the Northeast region of Brazil with approx-
imately 2.5 million inhabitants. Recently, the City Hall launched its smart city
program called “Fortaleza Inteligente”. This project, coordinated by the Foun-
dation for Science, Technology and Innovation of Fortaleza (CITINOVA), has as
one of its main components the city open data portal, which contains a plethora
of different datasets from a variety of public segments. We are collaborating with
CITINOVA to use our approach in the city open data portal, in particular to
represent data about the segment of urban mobility.
The first initiative was to model public transportation data. Fortaleza has
deployed, on each of its running buses, GPS instruments capable of transmitting
the current geographical position of the bus. GPS data is transmitted to a central
server where they are stored in datasets that follow an ad hoc logic. The files
should refer to a GPS position of a bus, for a given bus line and during a certain
day. In other words GPS data is represented by a triplet bus-id, bus-line, date.
However, this structure is not always followed. There are situations where the
file becomes too large, and is then divided into two datasets. In addition, when
a bus happens to be used in more than one line, then the same GPS receiver is
used to collect more than one bus line’s position. This can all be represented in
the same dataset, which again breaks the informal rule (bus-id, bus-line, date).
Other variations exist, but the decisive factor is that none of these informal
rules used for the construction of datasets were specified or clearly documented.
They were discovered in several rounds of interviews with providers of data only
when other users decided to explore the data. This makes the process not easily
reusable or scalable.
4.1 Building the Semantic Sensor Network
In order to develop a proof of concept, we gathered three datasets from the For-
taleza Dados Abertos7 portal that related to the public transportation system:
– Bus checkpoints: A list with all the bus checkpoints around the city. Each
checkpoint is composed of its code, a name, a lat/long position and a radius
around that lat/long to indicate the area the checkpoint that it covers.
– Bus companies: A list with all the bus companies on contract with City Hall
to provide that public service. Each company has a company id, a name and
other information.
7
http://dados.fortaleza.gov.br
Contextual Data Collection for Smart Cities 11
– Bus fleet: A list with all the buses running in the city. Each bus has an
id, a model, a maker, which company it belongs to and other information
including plates, serial numbers and so forth.
– GPS bus information for 2015/02: A report including when a specific bus
entered and left a bus checkpoint.
We note that first three datasets can be viewed as metadata that plays a
support role for the fourth dataset. This last dataset is the actual observed data
that was collected during a data collection activity. We also note that information
such as bus company and bus checkpoint provide contextual metadata. We have
mapped the checkpoints and fleet datasets using the HASNetO-SC ontology:
– Checkpoints: By analysing the GPS dataset, we can infer that all the mea-
surements contained inside it are coming from the checkpoints themselves.
To reflect this, we then mapped all checkpoints to individual instances of
the vstoi:Instrument as they are able to measure when a particular bus is
entering or leaving its coverage area.
– Checkpoints location: We have also inferred that the actual instruments iden-
tified in the previous item were deployed at the place described in it. For the
purpose of this proof of concept, we defined that the place of deployment is
an individual instance of hasneto-sc:RoadSegment, as all checkpoint’s lat/-
long locations are on roads.
The following is part of the Turtle describing our sensor network using
HASNetO-SC.
Serialized RDF Model of the Sensor Network
a v s t o i : Instrument ;
r d f s : l a b e l ” D a l l a s / Sobradinho /T F Paiva ” .
a v s t o i : Instrument ;
r d f s : l a b e l ” Pedro P e r e i r a / Imperador /T Goncalves ” .
...
a hasneto −s c : RoadSegment ;
r d f s : l a b e l ” D a l l a s / Sobradinho /T F Paiva ” .
geo : l a t −3.79486600 ;
geo : l o n g −38.61625700 .
a hasneto −s c : RoadSegment ;
r d f s : l a b e l ” Pedro P e r e i r a / Imperador /T Goncalves ” .
geo : l a t −3.72791200 ;
geo : l o n g −38.53405200 .
...
12 Henrique Santos et al.
4.2 Annotating the CSV dataset
Once the semantic sensor network was ready, the next step was to annotate the
CSV dataset containing the GPS bus information. The former dataset was a
combined collection of measurements from different checkpoint instruments. To
fully integrate this dataset with our process, we split it so that each resulting
dataset contains information collected by a single instrument. To annotate each
dataset, as mentioned before, we need a domain ontology that includes a hi-
erarchy of entities, characteristics and units. For the purpose of the use case,
we have developed a small ontology capable of describing the identified con-
cepts, i.e., to state that we are measuring the occurrence (unit) of an arrival
or departure (characteristic) of a bus (entity). As shown in Fig. 2, the ontology
defines two classes under the namespace pmf (acronym for Prefeitura Municipal
de Fortaleza - Fortaleza City Hall): pmf:BusArrivalDeparture that specializes
oboe:Characteristic and pmf:Binary that specializes oboe:BaseUnit. Also, note
that pmf:ArrivalDeparture characteristic has an associated entity pmf:Bus.
Given this, we annotated each dataset to build the following CCSV preamble.
GPS bus dataset CCSV preamble
a c c s v : KnowledgeBase ;
c c s v : hasConnectionURL ” h t t p . . . ” ˆ ˆ xsd : anyURI .
a v s t o i : Deployment ;
prov : startedAtTime ”2015−02−01T00 : 0 0 : 0 0 Z”ˆˆ xsd : dateTime ;
h a s n e t o : h a s D a t a C o l l e c t i o n .
a h a s n e t o : D a t a C o l l e c t i o n ; a time : I n t e r v a l ;
prov : startedAtTime ”2015−02−01T00 : 0 0 : 0 0 Z”ˆˆ xsd : dateTime .
a v s t o i : Dataset ;
prov : wasGeneratedBy ;
h a s n e t o : hasMeasurementType .
a oboe : Measurement ; a time : I n s t a n t ;
time : inDateTime ;
c c s v : atColumn 1 ;
oboe : o f C h a r a c t e r i s t i c pmf : A r r i v a l D e p a r t u r e ;
oboe : u s e s S t a n d a r d pmf : Binary .
a time : I n s t a n t ; c c s v : atColumn 0 .
Contextual Data Collection for Smart Cities 13
Fig. 2. Domain ontology for the bus use case.
The dataset was annotated to contain deployment information for that single
instrument and also to specify that a new data collection activity was being
initiated. It is also shown that this dataset has a measurement of characteristic
pmf:ArrivalDepartureEvent in the pmf:Binary unit located on column number
1 that was taken on the timestamp specified at column 0.
4.3 CCSV parsing and indexing
In order to complete the connection between data and metadata described pre-
viously including the full integration with the semantic sensor network, we have
deployed an instance of Apache SOLR8 to index and store both data and meta-
data. SOLR is a NoSQL database capable of indexing documents in a number of
different formats including CSV, XML and JSON. SOLR document collections
should include field definitions, including data types, for content that is to be
indexed and stored. Inside SOLR, we have created two collections: (i) a meta-
data collection for storing the semantic sensor network, the domain ontology and
the deployment metadata, and (ii) a measurement collection for storing the data
itself.
The metadata collection is basically a triple store where the sensor network
Turtle was loaded along with the domain ontology described. The measurement
collection is a regular SOLR collection with the following fields being indexed
and stored:
– Entity, characteristic and unit
– Location and timestamp
– Measured value
– Instrument
– Data collection to which the measurement belongs
To automate the process of storing this information, we have developed an
application called the CCSV-Loader9 . This loader performs, in order, the follow-
ing tasks:
1. Extract the Turtle preamble of the CCSV dataset
2. Get instrument information based on deployment
8
http://lucene.apache.org/solr/
9
http://tw.rpi.edu/web/project/JeffersonProjectAtLakeGeorge/download/ccsv
14 Henrique Santos et al.
3. Get platform information based on deployment
4. Generate a normalized CSV file with the extra columns containing metadata
information
5. Index the normalized CSV file in the measurement collection
Fig. 3. Faceted-browsing of Fortaleza city transportation data.
Once the data is indexed, we make use of SOLR faceted-search capabilities
to present a navigation UI to the user. Fig. 3 shows an example of that UI.
On the right side, measurements are shown, based on the user current search
parameters. On the left side, we have defined some fields to be faceted by SOLR
to provide easy access to commonly requested content. For this use case, we have
defined checkpoints as a field to be faceted so a user can select measurements
coming from a specific checkpoint. Also, we added buses and companies to the
facet field list, giving the ability for users to refine the search results by a bus or
a company.
Contextual Data Collection for Smart Cities 15
5 Discussion
This paper presented a streamlined process to collect, store and disseminate mon-
itored data in an urban environment. The work was motivated by the challenges
related to understanding, using, and integrating data from dataset portals based
on current monitoring and publishing approaches. One issue we introduced is the
boundary of a dataset as a natural context boundary vs. a technologically con-
venient boundary. Our Fortaleza public transportation system use case showed
that our approach enables users to have access not only to the sensor network
metadata but also to metadata driven navigation of the data. This combination
we believe helps circumvent many of the challenges with understanding, using,
and integrating data, even in settings where datasets have varied and potentially
arbitrary boundaries and also in settings where metadata is limited.
In future work, we will articulate and implement additional use cases. We
are also working on tighter integration with the real time sensor network content
in order to augment it using our metadata scheme thus further evaluating our
HASNetO-SC ontology. Additionally, we plan to apply our approach in other
Smart City initiatives. Our vision is to deploy our approach alongside open data
portals, providing end users with a better understanding of complex monitored
urban environment data.
Acknowledgements. The first author is supported by CNPq - Brazil.
References
1. Compton, M., Barnaghi, P., Bermudez, L., Garcı́a-Castro, R., Corcho, O., Cox,
S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., Huang, V., Janowicz,
K., Kelsey, W.D., Le Phuoc, D., Lefort, L., Leggieri, M., Neuhaus, H., Nikolov,
A., Page, K., Passant, A., Sheth, A., Taylor, K.: The SSN ontology of the W3c
semantic sensor network incubator group. Web Semantics: Science, Services and
Agents on the World Wide Web 17, 25–32 (Dec 2012)
2. Compton, M., Henson, C., Lefort, L., Neuhaus, H., Sheth, A.: A Survey of the
Semantic Specification of Sensors. CEUR Workshop Proceedings pp. 17–32 (Oct
2009)
3. Cox, S.: Observations and Measurements - XML Implementation (Mar 2011)
4. Fensel, A., Tomic, S., Kumar, V., Stefanovic, M., Aleshin, S.V., Novikov, D.O.:
SESAME-S: Semantic Smart Home System for Energy Efficiency. Informatik-
Spektrum 36(1), 46–57 (Dec 2012)
5. Fox, P., McGuinness, D.L., Cinquini, L., West, P., Garcia, J., Benedict, J.L.,
Middleton, D.: Ontology-supported scientific data frameworks: The Virtual Solar-
Terrestrial Observatory experience. Computers & Geosciences 35(4), 724–738 (Apr
2009)
6. Gao, F., Ali, M.I., Mileo, A.: Semantic Discovery and Integration of Urban Data
Streams. In: Proceedings of the Fifth Workshop on Semantics for Smarter Cities.
pp. 15–30. Riva del Garda, Italy (Oct 2014)
16 Henrique Santos et al.
7. Giffinger, R., Fertner, C., Kramar, H., Kalasek, R., Pichler-Milanovic, N., Mei-
jers, E.: Smart cities-Ranking of European medium-sized cities. Tech. rep., Vienna
University of Technology (2007)
8. Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. Geo-
Journal 69(4), 211–221 (Nov 2007)
9. Hendler, J., Holm, J., Musialek, C., Thomas, G.: US Government Linked Open
Data: Semantic.data.gov. IEEE Intelligent Systems 27(3), 25–31 (May 2012)
10. Kuhn, W.: A Functional Ontology of Observation and Measurement. In: Janowicz,
K., Raubal, M., Levashkin, S. (eds.) GeoSpatial Semantics, pp. 26–43. No. 5892 in
Lecture Notes in Computer Science, Springer Berlin Heidelberg (2009)
11. Lopez, V., Kotoulas, S., Sbodio, M.L., Stephenson, M., Gkoulalas-Divanis, A.,
Aonghusa, P.M.: QuerioCity: A Linked Data Platform for Urban Information Man-
agement. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J.,
Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist,
E. (eds.) The Semantic Web – ISWC 2012, pp. 148–163. No. 7650 in Lecture Notes
in Computer Science, Springer Berlin Heidelberg (Jan 2012)
12. Madin, J., Bowers, S., Schildhauer, M., Krivov, S., Pennington, D., Villa, F.: An
ontology for describing and synthesizing ecological observation data. Ecological
Informatics 2(3), 279–296 (Oct 2007)
13. Pinheiro, P., McGuinness, D.L., Santos, H.: Human-Aware Sensor Network Ontol-
ogy: Semantic Support for Empirical Data Collection. In: Proceedings of the 5th
Workshop on Linked Science. Bethlehem, PA, USA (2015)
14. Probst, F.: Ontological Analysis of Observations and Measurements. In: Raubal,
M., Miller, H.J., Frank, A.U., Goodchild, M.F. (eds.) Geographic Information Sci-
ence, pp. 304–320. No. 4197 in Lecture Notes in Computer Science, Springer Berlin
Heidelberg (2006)
15. Quine, W.V.O.: From Stimulus to Science. Harvard University Press (1995)
16. Rew, R., Davis, G.: NetCDF: an interface for scientific data access. IEEE Computer
Graphics and Applications 10(4), 76–82 (Jul 1990)
17. Shadbolt, N., O’Hara, K., Berners-Lee, T., Gibbins, N., Glaser, H., Hall, W.,
schraefel, m.: Linked Open Government Data: Lessons from Data.gov.uk. IEEE
Intelligent Systems 27(3), 16–24 (May 2012)
18. Stasch, C., Janowicz, K., Bröring, A., Reis, I., Kuhn, W.: A Stimulus-Centric
Algebraic Approach to Sensors and Observations. In: Trigoni, N., Markham, A.,
Nawaz, S. (eds.) GeoSensor Networks, pp. 169–179. No. 5659 in Lecture Notes in
Computer Science, Springer Berlin Heidelberg (2009)
19. Stavropoulos, T.G., Vrakas, D., Vlachava, D., Bassiliades, N.: BOnSAI: A Smart
Building Ontology for Ambient Intelligence. In: Proceedings of the 2Nd Inter-
national Conference on Web Intelligence, Mining and Semantics. pp. 30:1–30:12.
WIMS ’12, ACM, New York, NY, USA (2012)
20. Usbeck, R.: Combining Linked Data and Statistical Information Retrieval. In: Pre-
sutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) The
Semantic Web: Trends and Challenges, pp. 845–854. No. 8465 in Lecture Notes in
Computer Science, Springer International Publishing (Jan 2014)