P. Haase, K. Hose, Ralf Schenkel, Michael Schmidt, A. Schwarte
{"title":"Federated Query Processing over Linked Data","authors":"P. Haase, K. Hose, Ralf Schenkel, Michael Schmidt, A. Schwarte","doi":"10.1201/b16859-19","DOIUrl":"https://doi.org/10.1201/b16859-19","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126930591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Francesca Bugiotti, Jesús Camacho-Rodríguez, François Goasdoué, Zoi Kaoudi, I. Manolescu, Stamatis Zampetakis
{"title":"SPARQL Query Processing in the Cloud","authors":"Francesca Bugiotti, Jesús Camacho-Rodríguez, François Goasdoué, Zoi Kaoudi, I. Manolescu, Stamatis Zampetakis","doi":"10.1201/b16859-11","DOIUrl":"https://doi.org/10.1201/b16859-11","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123900829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jürgen Umbrich, Marcel Karnstedt, A. Polleres, K. Sattler
{"title":"Index-Based Source Selection and Optimization","authors":"Jürgen Umbrich, Marcel Karnstedt, A. Polleres, K. Sattler","doi":"10.1201/b16859-17","DOIUrl":"https://doi.org/10.1201/b16859-17","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117052604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient Query Processing in RDF Databases","authors":"Andrey Gubichev, Thomas Neumann","doi":"10.1201/b16859-8","DOIUrl":"https://doi.org/10.1201/b16859-8","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126656601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Linked Data & the Semantic Web Standards","authors":"A. Hogan","doi":"10.1201/b16859-3","DOIUrl":"https://doi.org/10.1201/b16859-3","url":null,"abstract":"On the traditional World Wide Web we all know and love, machines are used as brokers of content: they store, organize, request, route, transmit, receive, and display content encapsulated as documents. In order for machines to process the content of documents automatically—for whatever purpose— they primarily require two things: machine-readable structure and semantics. Unfortunately, despite various advancements in the area of Natural Language Processing (NLP) down through the decades, modern computers still struggle to meaningfully process the idiosyncratic structure and semantics of natural language due to ambiguities present in grammar, coreference and word-sense. Hence, machines require a more “formal” notion of structure and semantics using unambiguous grammar, referencing, and vocabulary.","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122702261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluating SPARQL Queries over Linked Data Streams","authors":"J. Calbimonte, Óscar Corcho","doi":"10.1201/b16859-9","DOIUrl":"https://doi.org/10.1201/b16859-9","url":null,"abstract":"So far we have addressed different aspects of RDF and Linked Data management, from modeling to query processing or reasoning. However, in most cases these tasks and operations are applied to static data. For streaming data, which is highly dynamic and potentially infinite, the data management paradigm is quite different, as it focuses on the evolution of data over time, rather that on storage and retrieval. Despite these differences, data streams on the Web can also benefit from the exposure of machine-readable semantic content as seen in the previous chapters. Semantic Web technologies such as RDF and SPARQL have been applied for data streams over the years, in what can be broadly called Linked Data Streams. Querying data streams is a core operation in any streaming data application. Ranging from environmental and weather station observations, to realtime patient health monitoring, the availability of data streams in our world is dramatically changing the type of applications that are being developed and made available in many domains. Many of these applications pose complex requirements regarding data management and query processing. For example, streams produced by sensors can help studying and forecasting hurricanes, to prevent natural disasters in vulnerable regions. Monitoring the barometric pressure at sea level can be combined with other wind speed measurements and satellite imaging to better predict extreme weather conditions1. Another example can be found in the health domain, where the industry has produced affordable devices that track caloric burn, blood glucose or heartbeat rates, among others, allowing live monitoring of the activity, metabolism, and sleep patterns of any person [226]. Moreover, data streams fit naturally with applications that store or publish them in the cloud, allowing ubiquitous access, aggregation, comparison,","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Linked Data Services","authors":"Sebastian Speiser, M. Junghans, A. Haller","doi":"10.1201/b16859-24","DOIUrl":"https://doi.org/10.1201/b16859-24","url":null,"abstract":"Information services are commonly provided via Web APIs based on Representational State Transfer (REST) principles [196,452] or via Web Services based on the WS-* technology stack [182,429]. Currently deployed information services use HTTP as transport protocol, but return data as JSON or XML which requires glue code to combine data from different APIs with information provided as Linked Data. Linked Data interfaces for services have been created, e.g., in form of the book mashup [97] which returns RDF data about books based on Amazon’s API, or twitter2foaf which encodes the Twitter follower network of a given user based on the API provided by Twitter. However, the interfaces are not formally described and thus the link between services and data has to be established manually or by service-specific algorithms. For example, to establish a link between person instances (e.g., described using the FOAF vocabulary1) and their Twitter account, one has to hard-code which property relates people to their Twitter username and the fact that the URI of the person’s Twitter representation is created by appending the username to http://twitter2foaf.appspot.com/id/. In this chapter, we present the LInked Data Services (LIDS) approach for creating Linked Data interfaces to information services. The approach incorporates formal service descriptions that enable (semi-)automatic service discovery and integration. Specifically, we present the following components: an access mechanism for LIDS interfaces based on generic Web architecture","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"2005 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using read-write Linked Data for Application Integration","authors":"A. L. Hors, Steve Speicher","doi":"10.1201/b16859-25","DOIUrl":"https://doi.org/10.1201/b16859-25","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"3 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115615133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"P2P-Based Query Processing over Linked Data","authors":"Marcel Karnstedt, K. Sattler, M. Hauswirth","doi":"10.1201/b16859-18","DOIUrl":"https://doi.org/10.1201/b16859-18","url":null,"abstract":"","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124158862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mapping Relational Databases to Linked Data","authors":"Juan Sequeda, Daniel P. Miranker","doi":"10.1201/b16859-7","DOIUrl":"https://doi.org/10.1201/b16859-7","url":null,"abstract":"To live up to its promise of web-scale data integration, the Semantic Web will have to include the content of existing relational databases. One study determined that there is 500 times as much data in the hidden or deep web as there is in crawlable, indexable web pages; most of that hidden data is stored in relational databases [79]. Starting with a 2007 workshop, titled “RDF Access to Relational Databases”1, the W3C sponsored a series of activities to address this issue. At that workshop, the acronym, RDB2RDF, Relational Database to Resource Description Framework, was coined. In September 2012, these activities culminated in the ratification of two W3C standards, colloquially known as Direct Mapping [43] and R2RML [165]. By design, both these standards avoid any content that speaks about implementation, directly or indirectly. The standards concern is syntactic transformation of the contents of rows in relational tables to RDF. The R2RML language includes statements that specify which columns and tables are mapped to properties and classes of a domain ontology. Thus, the language empowers a developer to examine the contents of a relational database and write a mapping specification. For relational databases with large database schema, the manual development of a mapping is a commensurately large undertaking. Thus, a standard direct mapping is defined; that is an automatic mapping of the relational data to an RDF graph reflecting the structure of the database schema. URIs are automatically generated from the names of database schema elements.","PeriodicalId":252334,"journal":{"name":"Linked Data Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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