!"#$%&'&(%)*+'"%),-&.%/)0%&1#")2'3') !"#$%"&'(")*+,'-")."'-/'-"%00,'10#.'!2*$0*,'34+)'-%2)%' 5*0/2/$+$'12*)2#,'10467)2#'-8+2*82'"*9':*;+*22#+*;'<26"#)42*),'=#+;%)'-)")2'>*+?2#$+).,' <".)0*,'@!'ABACB'>-3' !"#$%"#&'(%#)*#'(+,$*'(#-.)/012,3%.%4,$5( ' A bstract. 3URYHQDQFHIURPWKH)UHQFKZRUG³provenir´GHVFULEHVWKHOLQHDJHRUKLVWo- ry of a data entity. Provenance is critical information in the sensors domain to identify a sensor and analyze the observation data over time and geographical space. In this paper, we present a framework to model and query the provenance information associated with the sensor data exposed as part of the Web of Data using the Linked Open Data conventions. This is accomplished by developing an ontology-driven provenance man- agement infrastructure that includes a representation model and query infrastructure. This provenance infrastructure, called Sensor Provenance Management System (PMS), is underpinned by a domain specific provenance ontology called Sensor Provenance (SP) ontology. The SP ontology extends the Provenir upper level provenance ontology to model domain-specific provenance in the sensor domain. In this paper, we describe the implementation of the Sensor PMS for provenance tracking in the Linked Sensor Data. K eywords: Provenance Management Framework, provenir ontology, Provenance, Li- neage, Linked Data, Semantic Sensor Web, Sensor Data, Sensor Web Enablement, Da- taset Generation, Resource Description Framework (RDF) ' ' 45)6789:2;<86:7) ' The first North American blizzard of 2010 was tracked from the state of California to Arizona, through northern Mexico, and across the continental United States. The storm produced historic snowfall levels in the Mid-Atlantic States, as well as exten- sive flooding and landslides in Mexico. During this time, a number of weather sta- tions collected data from thousands of sensors deployed in the United States. Seman- tic Sensor Web1 proposes to annotate this sensor data with semantic metadata to provide contextual information essential for situational awareness. Such semantic metadata data can be used to answer aggregate queries spanning both temporal and geographical areas. Let us consider the following scenario. We are interested in finding all the sensors which have observations related to a blizzard of interest. In order to accomplish this task, we would need to know the properties associated with a phenomenon to be clas- sified as a blizzard, the time period for which the blizzard was active, the location where the blizzard occurred, and sensors deployed in this location during this time period. ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''' D'%))6EFFG+H+/H*02$+$/0#;F+*92I/6%6F--=' This is an example of a sensor discovery query. Sensor discovery has been identified as a top-priority use case by the W3C Semantic Sensor Network Incubator Group 2, which is tasked with development of sensor ontology. In the sensors domain, the capabilities of the sensor, observation location (spatial parameter), time of observa- tion (temporal parameter), and phenomenon measurement (domain parameter) are important to answer discovery queries. This data related to the sensor is the prove- nance metadata about the sensor. Provenance describes the history or the lineage of an entity and is GHULYHG IURP WKH )UHQFK ZRUG ³provenir´ PHDQLQJ ³WR FRPH IURP´. Provenance informatioQHQDEOHVDSSOLFDWLRQVWRDQVZHUWKH³ZKDW´³ZKHUH´³ZK\´ ³ZKR´³ZKLFK´³ZKHQ´DQG³KRZ´TXHULHVWRDFFXUDWHO\LQWHUSUHWDQGSURFHVVGDWD entities. Provenance has been studied from multiple perspectives, including (a) workflow provenance and (b) database provenance as discussed in Tan [1]. Workflow prove- QDQFH UHSUHVHQWV ³WKH HQWLUH KLVWRU\ RI WKH GHULYDWLRQ RI WKH ILQDO RXWSXW RI´ [1] a workflow. Davidson et al. [2] addresses issues related to provenance in workflow systems. In contrast, database provenance refers to the process of tracing and record- ing the origins of data and its movement between databases [3]. In Sahoo et al. [4], we introduced the notion of semantic provenance to define provenance information that incorporates domain semantics to closely reflect the knowledge of an application domain. In this paper, we use the observations from the 20,000 sensors within the United States (Figure 1) in the context of a blizzard as a running example. F ig.1.The distribution of 20,000 Sensors constituting the Semantic Sensor Web (SensorMap Image [5]) We use the definition of a blizzard provided by the NOAA3, which describes it as: B L I Z Z A R D = High WindSpeed (exceeding 35 mph) AND Snow Precipita- tion AND Low Visibility (less than ! mile), for at minimum 3 hours. F ig.2. Blizzard Composition !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! "!#$$%&''((()(*)+,-'"../'012345$+,'661'(787'9571:;5-+.0-,?..@+.'A0,6+3>/$/)#$41!! ! 3. C ur rent Infrastructure ! The lifespan of sensor data starts as observable properties of objects and events in the real-world which are detected by sensors through observation. These observation values are then encoded in several formats of varying degrees of expressivity, as needed by applications that may utilize the data. The data generation workflow is comprised of four main parts, as shown in figure 3. The workflow begins with sensors deployed across the United States measuring environmental phenomena. Observations generated from these sensors are aggregated at MesoWest [13] which provides access to past sensor observations encoded as comma separated numerical values. These sensor observations are then converted to Observations and Measurements (O&M). O&M is an encoding standard and a technical framework that defines an abstract model and an XML schema encoding for sensor descriptions and observations. It is one of OGC9 Sensor Web Enablement (SWE)10 suite of standards that is widely accepted within the sensors community for encoding sensor observations. [14] In order to add semantics to the sensor descriptions and observations the O&M is con- verted to RDF. O&M is converted to RDF using the O &M2RD F-Converter API de- scribed in [15]. Two RDF datasets, !"#$%&'%#()*+,-,. "#$! !"#$%&/0(%*1,-")#+,-,. %&#'"(#(#)! &*+,! "! -(..(&#! ',(/.+0. 1+,+! )+#+,"'+$2! 34+! $"'"0+'0! ",+! $+0%,(-+$! (#! '4+! 0+%'(&#!52 The RDF generated is then stored in a Virtuoso RDF knowledgebase [16]. The RDF datasets are made available on the Linked Open Data Cloud to provide public access. The data generation workflow is the main component of the Prove- nance Capture phase discussed in Section 5. ! F ig.3. Data Generation Workflow. The O&M to RDF conversion (dotted portion) forms the main part of the workflow that uses the O &M2RD F-C ONVERTER API . 3.1 Phase 1 ± The first phase is comprised of querying MesoWest [13] for observa- tional data and parsing the result. MesoWest provides a service to access past sensor data and returns an HTML page with the observational values encoded within a com- ma-separated list. The resulting HTML page is then parsed to extract the sensor ob- servations. 3.2 Phase 2 ± The second phase consists of converting the raw textual data retrieved from MesoWest into O&M. The sensor observations parsed from the HTML page in phase 1 are fed to an XML parser. We used the SAX (Simple API for XML) parser 11 to generate the O&M. Here we also query GeoNames [17] with the sensor coordinates to get GeoNames location that is closest to the sensor. The O&M generated in this phase is the input for the O &M2RD F-Converter API . !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 6 !4''/7881112&/+#)+&0/"'(".2&,)8! 9:!4''/7881112&/+#)+&0/"'(".2&,)8/,&;+%'08),&:*#1; 1+9#$%#12*+:'+,)1$,/$?5@A@@@$-#&'"#*$1'&'+,)1$+)$'"#$B)+'#%$='&'#1C$!"#$%&'&$,*+(+; )&'#%$&'$D#1,E#1'A$&$:*,F#2'$-+'"+)$'"#$7#:&*'G#)'$,/$D#'#,*,0,(H$&'$'"#$B)+; 9#*1+'H$,/$B'&"$'"&'$"&1$3##)$&((*#(&'+)($-#&'"#*$%&'&$1+)2#$5@@5C$IJ4K$L)$&9#*; &(#A$ '"#*#$ &*#$ /+9#$ 1#)1,*1$ :#*$ -#&'"#*$ 1'&'+,)$ G#&1M*+)($ :"#),G#)&$ 1M2"$ &1$ '#G:#*&'M*#A$9+1+3+0+'HA$:*#2+:+'&'+,)A$:*#11M*#A$-+)%$1:##%A$"MG+%+'HA$#'2C$N)$&%%+; '+,)$',$0,2&'+,)$&''*+3M'#1$1M2"$&1$0&'+'M%#A$0,)(+'M%#A$&)%$#0#9&'+,)A$'"#*#$&*#$0+).1$ ',$ 0,2&'+,)1$ +)$ O#,)&G#1$ IJPK$ )#&*$ '"#$ -#&'"#*$ 1'&'+,)C$ !"#$ %+1'&)2#$ /*,G$ '"#$ O#,)&G#1$0,2&'+,)$',$'"#$-#&'"#*$1'&'+,)$+1$&01,$:*,9+%#%C$!"#$%&'&$1#'$&01,$2,); '&+)1$0+).1$',$'"#$ G,1'$2M**#)'$,31#*9&'+,)$/,*$#&2"$ -#&'"#*$1'&'+,)$:*,9+%#%$3H$ D#1,E#1'$IJ4KC$!"+1$1#)1,*1$%#12*+:'+,)$%&'&1#'$+1$),-$:&*'$,/$'"#$:*#11+9#$ %#12*+:'+,)1$ ,/$ "M**+2&)#$ &)%$ 30+QQ&*%$ ,31#*9&'+,)1$ +)$ '"#$ B)+'#%$='&'#1C$!"#$%&'&$&(&+)$,*+(+)&'#%$&'$D#1,E#1'C$IJ4K$!"#$,31#*9&'+,)1$2,0; 0#2'#%$+)20M%#$G#&1M*#G#)'1$,/$:"#),G#)&$1M2"$&1$'#G:#*&'M*#A$9+1+3+0+'HA$:*#2+; SLWDWLRQSUHVVXUHZLQGVSHHGKXPLGLW\HWF7KHZHDWKHUVWDWLRQ¶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ensor Provenance M anagement System ! The Sensor PMS infrastructure uses the data generation workflow described above (section 3) and addresses three aspects of provenance management as identified by [20]. See Figure 4 for an architecture of Sensor PMS. ! "#$%&%!"#$%&'"()#')*&#$+,$)"#$!"#$%&'-./$%00 (23$ !"&##$%14#')1$+,$4&+5#2%2'#$6%2%3#6#2)$ $ 1. Provenance C apture ± The provenance information associated with the sensor is captured within the data workflow as described in section 3. The time related information (temporal parameter) is obtained from MesoWest [13] and location related information (spatial parameter) is obtained by que- rying GeoNames [17] with the sensor coordinates. 2. Provenance Representation ± The Sensor Provenance ontology (SP) is used to model the provenance information related to the sensor. The SP on- tology extends the Provenir upper level provenance ontology defined in PMF [4] to support interoperability with provenance ontology in different do- mains. 3. Provenance Storage ± The provenance information is stored in the Virtuoso RDF store. Virtuoso RDF is an open source triple store provided by Open- Link Software.[16] The Virtuoso RDF store currently contains over a billion triples of sensor observational data. Virtuoso RDF provides a SPARQL end- point to query these dataset discussed in section 4, which can be found at [21]. More information about querying the dataset can be found at [19]. 6. Sensor Provenance O ntology ! In this section we discuss the Sensor Provenance Ontology that forms the key compo- nent of the Sensor PMS. As discussed above, provenance information includes the location of the sensor, the time when the observations were taken by the sensor and the sensor observation values. Since SP ontology extends the provenir ontology, we discuss the provenir ontology in section 6.1 followed by SP ontology in section 6.2 6.1 Provenir O ntology - Provenir ontology is a common provenance model which forms the core component of the provenance management framework. [4] This mod- ular framework forms a scalable and flexible approach to provenance modeling that can be adapted to the specific requirement of different domains. Use of Provenir on- tology as the reference model to built domain-specific provenance ontologies ensures (a) common modeling approach, (b) conceptual clarity of provenance terms, and (c) use of design patterns for consistent provenance modeling F ig.5. Provenir Upper Level Ontology [4] The ontology defines three base classes data , agent and process using the well de- fined, primitive concepts of occurent and continuant. [22] Continuant is defined as ³HQWLWLHVZKLFKHQGXUHRUFRQWLQXHWRH[LVWWKURXJKWLPHZKLOHXQGHUJRLQJGLIIHUHQW VRUWV RI FKDQJHV LQFOXGLQJ FKDQJHV RI SODFH´ >22] while Occurrent is defined as ³HQWiWLHV WKDW XQIROG WKHPVHOYHV LQ VXFFHVVLYH WHPSRUDO SKDVHV´. [22]. The two base classes, data and agent are defined as specialization (sub-class) of continuant class while the third base class process is a synonym of occurent. The data class has two sub-classes, data_collection -- that represents the datasets that undergo modification during an experiment -- and para meter -- that influences the execution of an experi- ment. The para meter class has three sub-classes representing the spatial, temporal, and thematic (domain-specific) dimensions, namely spatial_para meter , tempor- al_para meter, and domain_para meter. Instead of defining a new set of properties, the ontology reuses and adapts properties defined in the Relation ontology (RO) 12 from the Open Biomedical Ontologies (OBO) Foundry13 such as part_of, contained_in, preceded_by, and has_participant. The Provenir ontology is defined using OWL- DL14 that is complaint with the DL profile of OWL2 15, with an expressivity of! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! "# !$%%&'(()))*+,+-+./012*+13(1+(!! "4!$%%&'(()))*+,+-+./012*+13(!! "5!$%%&'(()))*)4*+13(67(+)89-:;%.1:<(!! !"#$!" further details of the ontology can be found at [23]. Figure 5 shows the Provenir ontology schema obtained from [4]. 5.2 Sensor O ntology - E xtending Provenir O ntology The Provenir ontology has been extended to create the Sensor ontology that models the domain-specific provenance information for the sensor domain. The Sensor ontol- ogy extends the relevant Provenir ontology terms using the rdfs:subClassOf and rdfs:subPropertyOf relationships to create appropriate classes and properties. For example, the sensor:ResultData (representing the observation value) is a subclass of provenir:data_collection, the sensor:Location class (representing the geographical location) is defined as a subclass of provenir:spatial_para meter . Similarly, sen- sor:sa mplingTime is defined as a subproperty of provenir:has_temporal_value . The sensor ontology has been defined in OWL-DL and consists of 89 classes, 53 properties with a DL expressivity of" !"%$&'()*+#" By extending the Provenir ontology, the sensor ontology ensures coherent modeling of concepts, consistent use of provenance terminology, and compatibility with other existing domain-specific provenance ontologies. For example, the Trident ontology extends the Provenir ontol- ogy to model provenance information in the Neptune oceanography project [24]. In the next section, we describe the queries that utilize the provenance information mod- eled in the sensor ontology. ! 7. Provenance Q ueries Two classes of Provenance queries have been categorized by PMF [4]. Corresponding queries in the sensors domain that could not be answered without provenance infor- mation have been provided. 1. Q uery for provenance metadata: Given a data entity, this category of queries returns the complete set of provenance information associated with a data entity. E xample: ³ Given an observation value, give me the provenance information about the all the sensors that recorded this observation´ SELECT ?sensor ?ID ?geonamesLocation ?geonamesDistance ?geonamesDistanceMeasure ?latitude ?longitude ?observedProperty ?XSDTime WHERE {?sensor om-owl:generatedObservation ?generatedObservation . ?generatedObservation om-owl:observedProperty ?observedProperty . ?generatedObservation om-owl:result ?measureData . ?measureData om-owl:floatValue ?value . FILTER(?value = "78.0"^^xsd:float) . ?generatedObservation om-owl:samplingTime ?timeInstant . ?timeInstant owl-time:inXSDDateTime ?XSDTime . ?sensor om-owl:ID ?ID . ?sensor om-owl:hasLocatedNearRel ?locatedNear . ?locatedNear om-owl:hasLocation ?geonamesLocation . ?locatedNear om-owl:distance ?geonamesDistance . ?locatedNear om-owl:distanceUOM ?geonamesDistanceMeasure . ?sensor om-owl:processLocation ?sensorLocation . ?sensorLocation wgs84:lat ?latitude . ?sensorLocation wgs84:long ?longitude . } """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" $%"&''()**+++#+,#-./*01*-+234(.-56278*"" 2. Q uery for data using provenance information: An opposite perspective to the first category of query is, given a set of constraints defined over provenance in- formation retrieve a set of data entities satisfying some set of constraints. Exam- ple: ³ F ind all the sensors which have observations related to a blizzard occur- ring in Nevada on 24th August 2005 at 11 AM ´ To solve this sensor discover query, provenance information describing the spa- tio-temporal and thematic aspects of sensor observations and sensors can be ana- lyzed. Figure 6 describes the multiple steps followed in identifying the appropri- DWHVHQVRU,Q6WHSVHQVRUVORFDWHGLQWKH³1HYDGD´UHJLRQDUHidentified (from a pool of 20,000 sensors located across the United State). In Step 2, the sensors that were active during the blizzard are identified, and finally in Step 3 prove- nance information describing the capabilities of a sensor help identify the obser- vations that are relevant for the blizzard under study (for example, a wind speed sensor is considered relevant while a motion sensor is not considered relevant.) ! F ig.6. Answering a sensor-discovery query using spatio-temporal, and thematic prov- enance information! ! 8. Related Wor k Although this is the first attempt to develop an infrastructure for Sensor Provenance Management, there have been successful attempts to do the same in the domain of e- science. Within the sensors domain, provenance has been addressed from the storage point of view. Provenance management within the eScience community has primarily been ad- dressed in the context of workflow engines [25] while provenance management issues have been surveyed by Simmhan et al. [26]. The database community has also ad- dressed the issue of provenance and defined various types of provenance, for example ³ZK\SURYHQDQFH´>7] anG³ZKHUHSURYHQDQFH´>7]. A detailed comparison of PMF (that underpins the Sensor PMS) with both workflow and database provenance is presented in [4]. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) [28] is an exam- ple of eScience project with dedicated infrastructure for provenance management. In contrast to the Sensor PMS, the SPCDIS project uses the proof markup language (PML) [29] to capture provenance information. The Inference Web toolkit [29] fea- tures a set of tools to generate, register and search proofs encoded in PML. Both Sen- sor PMS and the SPCDIS have common objectives but use different approaches to achieve them, specifically the Sensor PMS uses an ontology-driven approach with robust query infrastructure for provenance management. In the Sensors community, Ledlie et al. [30] show how provenance addresses the naming and indexing issues related to sensor data storage. Park et al. [31] explore the need for data provenance in Sensornet Republishing, a process of transforming on- line sensor data and sharing the filtered, aggregated, or improved data with others. 9. Conclusion This paper introduces an in-use ontology-driven provenance management infrastruc- ture for Sensor data called Sensor PMS. We have developed a domain specific sensor provenance ontology by extending the provenir ontology. Due to this extension, SP ontology can interoperate with other domain-specific provenance ontologies to facili- tate sharing and integration of provenance information from different domains and projects. We also show how provenance information can help answer complex que- ries within the sensors domain. A cknowledgments. This work is funded in part by NIH RO1 Grant# 1R01HL087795-01A1 The Dayton Area Graduate Studies Institute (DAGSI), AFRL/DAGSI Research Topic SN08-8: "Architectures for Secure Semantic Sensor Networks for Multi-Layered Sensing.". !"#"$"%&"'( ! [1] W. C. Tan. Provenance in Databases: Past, Current, and Future. IEE E Data Engineering Bulletin, 30(4):3±12, Dec. 2007. [2] S. B. Davidson, S. C. Boulakia, A. Eyal, B.Lud¨ascher, T. M. McPhillips, S. Bowers, M. K.Anand, and J. Freire. Provenance in Scientific Workflow Systems. IE E E Data Engi- neering Bulletin, 30(4):44±50, Dec. 2007. [3] P. Buneman, S. Khanna, and W. C. Tan. Data Provenance: Some Basic Issues. In Pro- ceedings of the 20th Conference on Foundations of Software Technology and Theoretical Computer Science (F S T TC S). Springer, Dec. 2000. [4] S.S. Sahoo, R.S. Barga, J. Goldstein, A.P. Sheth, K. Thirunarayan, "Where did you come from...Where did you go?" An Algebra and RDF Query Engine for Provenance Kno.e.sis Center, Wright State University; 2009. 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Patni, C. Henson, A. Sheth, 'Linked Sensor Data,' In: Proceedings of 2010 International Symposium on Collaborative Technologies and Systems (CTS 2010), Chicago, IL, May 17-21, 2010. [16] Openlink Software. http://www.openlinksw.com/, Retrieved March 12 2010 [17] GeoNames. http://www.geonames.org/, Retrieved March 12 2010 [18] XSLT. http://www.w3.org/TR/xslt, Retrieved March 12 2010 [19] SSW Dataset. http://wiki.knoesis.org/index.php/SSW_Datasets, Retrieved March 12 2010 [20] S. S. Sahoo, D. B. Weatherly, R. Mutharaju, P. Anantharam, A. P. Sheth, R. L. Tarleton, ³Ontology-Driven Provenance Management in eScience: An Application in Parasite Re- search.´ OTM Conferences (2) 2009: 992-1009 [21] Virtuoso SPARQL endpoint. http://harp.cs.wright.edu:8890/sparql, Retrieved March 14 2010 [22] B. Smith, W. Ceusters, B. Klagges, J. Kohler, A. Kumar, J. Lomax, et al., ³Relations in biomedical ontologies.´ Genome Biol 2005;6(5):R46. [23] Provenir Ontology. http://knoesis.wright.edu/library/ontologies/provenir/provenir.owl, Retrieved March 13 2010 [24] 666DKRR$6KHWK³3URYHQLUontology: Towards a Framework for eScience Prove- QDQFH0DQDJHPHQW´0LFURVRIWH6FLHQFH:RUNVKRS86$2FW [25] Provenance Challenge Wiki. http://twiki.ipaw.info/bin/view/Challenge/WebHome, Re- trieved March 11 2010 [26] Y.L. Simmhan, B. Plale, D. Gannon, ³A survey of data provenance in e-science´ SIGMOD Rec. 2005;34( 3):31 - 36 [27] P. Buneman, S. Khanna, W.C. Tan, ³Why and Where: A Characterization of Data Prove- nance.´ In: 8th International Conference on Database Theory; 2001; 2001. p. 316 - 330 [28] SPCDIS. http://spcdis.hao.ucar.edu/, Retrieved March 11 2010 [29] Inference Web. http://iw.stanford.edu/2.0/, Retrieved March 11 2010 [30] J. Ledlie, C. Ng, D. A. Holland, K.-K. Muniswamy-Reddy, U. Braun, and M. Seltzer. ³Provenance-aware sensor data storage.´ In NetDB 2005, April 2005. [31] U. Park, J. Heidemann. ³Provenance in Sensornet Republishing.´ In Proceedings of the 2nd International Provenance and Annotation Workshop , pp. 208-292. Salt Lake City, Utah, USA, Springer-Verlag. June, 2008