Use of the SAND spatial browser for digital government applications

2013

Proc. VLDB Endow.

Self Cite

Tabular data is an abundant source of information on the Web, but remains mostly isolated from the latter's interconnections since tables lack links and computer-accessible descriptions of their structure. In other words, the schemas of these tables -attribute names, values, data types, etc. -are not explicitly stored as table metadata. Consequently, the structure that these tables contain is not accessible to the crawlers that power search engines and thus not accessible to user search queries. We address this lack of structure with a new method for leveraging the principles of table construction in order to extract table schemas. Discovering the schema by which a table is constructed is achieved by harnessing the similarities and differences of nearby table rows through the use of a novel set of features and a feature processing scheme. The schemas of these data tables are determined using a classification technique based on conditional random fields in combination with a novel feature encoding method called logarithmic binning, which is specifically designed for the data table extraction task. Our method provides considerable improvement over the wellknown WebTables schema extraction method. In contrast with previous work that focuses on extracting individual relations, our method excels at correctly interpreting full tables, thereby being capable of handling general tables such as those found in spreadsheets, instead of being restricted to HTML tables as is the case with the WebTables method. We also extract additional schema characteristics, such as row groupings, which are important for supporting information retrieval tasks on tabular data.

Section: Discussionmentioning

confidence: 99%

Schema extraction for tabular data on the web

Adelfio

2013

Proc. VLDB Endow.

Self Cite

“…The VASCO system can be found at http://www.cs.umd.edu/˜hjs/quadtree/. For an example of their use in a spatial database/geographic information system (GIS), see the SAND Spatial Browser (Brabec and Samet, 2007;Esperança and Samet, 2002;Samet et al, 2003) and the QUILT system . Such systems find use in a number of alternative application domains (e.g., digital government (Marchionini et al, 2003)).…”

Section: Discussionmentioning

confidence: 99%

Sorting Spatial Data by Spatial Occupancy

GeoSpatial Visual Analytics

2009

Self Cite

Abstract. The increasing popularity of web-based mapping services such as Microsoft Virtual Earth and Google Maps/Earth has led to a dramatic increase in awareness of the importance of location as a component of data for the purposes of further processing as a means of enhancing the value of the nonspatial data and of visualization. Both of these purposes inevitably involve searching. The efficiency of searching is dependent on the extent to which the underlying data is sorted. The sorting is encapsulated by the data structure known as an index that is used to represent the spatial data thereby making it more accessible. The traditional role of the indexes is to sort the data, which means that they order the data. However, since generally no ordering exists in dimensions greater than 1 without a transformation of the data to one dimension, the role of the sort process is one of differentiating between the data and what is usually done is to sort the spatial objects with respect to the space that they occupy. The resulting ordering should be implicit rather than explicit so that the data need not be resorted (i.e., the index need not be rebuilt) when the queries change. The indexes are said to order the space and the characteristics of such indexes are explored further.

“…Our proposed setup is very desirable from a systems point of view because, now, all operations can be performed in the context of a database system. Our work opens up the use of a commercial database for building interactive applications (e.g., GIS applications [32]) on spatial networks. The real success of our method lies not so much in being able to succinctly capture the n 2 network distances in a spatial network using only OðnÞ space, but rather in the ability of our oracle construct to be seamlessly integrated into a relational database system, indexed using a traditional indexing scheme (e.g., B-tree), and used in complicated query processing scenarios without making any significant changes to existing database systems.…”

Section: Query Processingmentioning

confidence: 99%

Query Processing Using Distance Oracles for Spatial Networks

Sankaranarayanan

IEEE Trans. Knowl. Data Eng.

2010

Self Cite

Abstract-The popularity of location-based services and the need to do real-time processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge here is that the efficient execution of spatial operations usually involves the computation of distance along a spatial network instead of "as the crow flies," which is not simple. Techniques are described that enable the determination of the network distance between any pair of points (i.e., vertices) with as little as OðnÞ space rather than having to store the n 2 distances between all pairs. This is done by being willing to expend a bit more time to achieve this goal such as Oðlog nÞ instead of Oð1Þ, as well as by accepting an error " in the accuracy of the distance that is provided. The strategy that is adopted reduces the space requirements and is based on the ability to identify groups of source and destination vertices for which the distance is approximately the same within some ". The reductions are achieved by introducing a construct termed a distance oracle that yields an estimate of the network distance (termed the "-approximate distance) between any two vertices in the spatial network. The distance oracle is obtained by showing how to adapt the well-separated pair technique from computational geometry to spatial networks. Initially, an "-approximate distance oracle of size Oð n " d Þ is used that is capable of retrieving the approximate network distance in Oðlog nÞ time using a B-tree. The retrieval time can be theoretically reduced further to Oð1Þ time by proposing another "-approximate distance oracle of size Oð n log n " d Þ that uses a hash table. Experimental results indicate that the proposed technique is scalable and can be applied to sufficiently large road networks. For example, a 10-percentapproximate oracle (" ¼ 0:1) on a large network yielded an average error of 0.9 percent with 90 percent of the answers having an error of 2 percent or less and an average retrieval time of 68 seconds. The fact that the network distance can be approximated by one value is used to show how a number of spatial queries can be formulated using appropriate SQL constructs and a few built-in primitives. The result is that these operations can be executed on almost any modern database with no modifications, while taking advantage of the existing query optimizers and query processing strategies.