XML mapping technology: Making connections in an XML-centric world

Roth, Mary T.; Hernández, Mauricio A.; Coulthard, Phil; Yan, Limei; Popa, Lucian; Ho, H.; Salter, C. C.

doi:10.1147/sj.452.0389

Cited by 20 publications

(12 citation statements)

References 22 publications

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“…The spreadsheet data can then be converted into a database by the user explicitly specifying the source and target attribute mapping using tools such as Clio [9] or by using low-level transformation languages such as XSLT [4]. The key disadvantage in this approach is that such humancontrolled mapping is specific to each spreadsheet and thus needs to be done for each spreadsheet individually.…”

Section: Contributionsmentioning

confidence: 99%

“…The reason for choosing this technique is two-fold: one, the technique doesn't assume that features are independent (as opposed to techniques using the Hidden Markov Model [13]) and two, future observations of the same entity type classified earlier are taken into account while labeling entity instances (as opposed to techniques using Maximum Entropy Models [4]). For example, our CRF-based classifier correctly distinguishes between an entity Location like the city "New York" and an Organization like "New York Times" that contains the same prefix as the Location entity.…”

Section: Definition 5 Entity Label: the Entity Classifier Assigns A Lmentioning

confidence: 99%

“…They would thus require users to have an in-depth knowledge of the data and to learn mapping languages [4] and operators [5]. Others [1,2] require users to specify explicit conversion rules that can be difficult and time consuming for the user to compose.…”

Section: Introductionmentioning

confidence: 99%

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Using entity identification and classification for automated integration of spatial-temporal data

Ahsan¹,

Neamtu²,

Rundensteiner³

2016

Int. J. DNE

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Big data, crucial to answering economic, social, and political questions facing our society, tend to be diverse and distributed through various sites across the Internet. The creation of tools to integrate and analyze such data is of paramount interest. Yet the automation of these processes continues to be a great challenge. Our work rests on the observation that a great number of public data sources in domains ranging from economic to demographic, although of complex structure, often share key similarities, namely the presence of the Time and Location. Our proposed Data Integration through Object Modeling framework or DIOM tackles the critical problem of automating data integration from a variety of public websites by abstracting key features of multi-dimensional tables and interpreting them in the context of knowledge-centered Unified Spatial Temporal Model. Our classification-driven extractors are trained to identify and classify entities from both structured and unstructured parts of spreadsheets. The unstructured part contained in titles, headers, and footers reveals critical information, so-called Implicit Knowledge, crucial to the correct interpretation of data. Our experimental results on real world datasets from heterogeneous public data sources show increased accuracy by 25% compared to state-of-the-art approaches.

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Section: Contributionsmentioning

confidence: 99%

Section: Definition 5 Entity Label: the Entity Classifier Assigns A Lmentioning

confidence: 99%

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Using entity identification and classification for automated integration of spatial-temporal data

Ahsan¹,

Neamtu²,

Rundensteiner³

2016

Int. J. DNE

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“…While the unification of data from heterogeneous sources is a complex task and domain-dependent, there are well understood approaches to tackling data integration issues [9]. More recently, XML mapping technologies have been studied in terms of processing and transforming XML data [20] [19] and XML sensor streams [18] with solutions to optimise XML views [13] and efficiently update XML [16]. However, none of this research looked at the generation of OLAP-ready cubes from the XML data sources.…”

Section: Introductionmentioning

confidence: 99%

Generating cubes from smart city web data

Scriney

O’Connor

Roantree

2017

Proceedings of the Australasian Computer Science Week Multiconference

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A smart city necessitates the incorporation of data sources from multiple providers and data formats. Similar to the Internet Of Things, this data is primarily obtained from web streams producing XML or JSON data. Various combinations of data obtained from different providers can be used to enhance the lives of citizens with respect to different characteristics such as transport, city planning, the environment and housing. However, data provided from these streams is not necessarily in a format suitable for analysis and OLAP queries, despite the fact that these streams often provide measures and some elements of dimensionality often found in OLAP queries. In this research, we present a StarGraph construct which is designed to import web generated data streams and automatically generate the cube format necessary for OLAP queries. Our validation shows how the data streams can be captured as StarGraphs and using a traffic data case study, demonstrates an efficiency for populating and updating the data cube.

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“…The first approach, namely schema-based, allows users to specify schemas of spreadsheets via a layout specification language [7], and then transformation can be performed at schema level using either low-level transformation languages (e.g., XSLT), or high-level mapping tools, such as Clio [5], Clip [10], and +Spicy [9]. However, users must learn a new language, e.g., by creating correspondences between the source and target elements and annotating those correspondences with one or more unfamiliar functions (e.g., functions of XSLT/XQuery, Java) in the case of mapping tools [13]. This flowchartlike mapping interface is cluttered when schemas are large and mappings are complex [12].…”

Section: Introductionmentioning

confidence: 99%

Spreadsheet-based complex data transformation

Hung

Benatallah²,

Saint-Paul

2011

Proceedings of the 20th ACM International Conference on Information and Knowledge Management

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Spreadsheets are used by millions of users as a routine allpurpose data management tool. It is now increasingly necessary for external applications and services to consume spreadsheet data. In this paper, we investigate the problem of transforming spreadsheet data to structured formats required by these applications and services. Unlike prior methods, we propose a novel approach in which transformation logic is embedded into a familiar and expressive spreadsheet-like formula mapping language. Popular transformation patterns provided by transformation languages and mapping tools are supported in the language via formulas. Consequently, the language avoids cluttering the source spreadsheets with transformations and turns out to be helpful when multiple schemas are targeted. We implemented a prototype and evaluated the benefits of our approach via experiments in a real application. The experimental results confirmed the benefits of our approach.

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XML mapping technology: Making connections in an XML-centric world

Cited by 20 publications

References 22 publications

Using entity identification and classification for automated integration of spatial-temporal data

Using entity identification and classification for automated integration of spatial-temporal data

Generating cubes from smart city web data

Spreadsheet-based complex data transformation

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