Google Earth Engine: Planetary-scale geospatial analysis for everyone

We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

/pdf/yago-a-core-of-semantic-knowledge-13k3nmj6l2.pdf

Yago: a core of semantic knowledge

Social Network Analysis

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

The present report is submitted in response to General Assembly resolution 61/106, by which the Assembly adopted the Convention on the Rights of Persons with Disabilities and the Optional Protocol thereto. As requested by the Assembly, a brief overview of the status of the Convention as at 3 August 2007 is presented. The report also contains a brief description of technical arrangements on staff and facilities made necessary for the effective performance of the functions of the Conference of States Parties and the Committee under the Convention and the Optional Protocol, and a description on the progressive implementation of standards and guidelines for the accessibility of facilities and services of the United Nations system. Recent efforts of the United Nations and agencies to disseminate information on the Convention and the Optional Protocol are also described.

わかりやすい障害者の権利条約 : 知的障害のある人の権利のために = Convention on the rights of persons with disabilities

Google Fusion Tables is a cloud-based service for data management and integration. Fusion Tables enables users to upload tabular data files (spreadsheets, CSV, KML), currently of up to 100MB. The system provides several ways of visualizing the data (e.g., charts, maps, and timelines) and the ability to filter and aggregate the data. It supports the integration of data from multiple sources by performing joins across tables that may belong to different users. Users can keep the data private, share it with a select set of collaborators, or make it public and thus crawlable by search engines. The discussion feature of Fusion Tables allows collaborators to conduct detailed discussions of the data at the level of tables and individual rows, columns, and cells. This paper describes the inner workings of Fusion Tables, including the storage of data in the system and the tight integration with the Google Maps infrastructure.

/pdf/google-fusion-tables-data-management-integration-and-7ih7dwn1or.pdf

Google fusion tables: data management, integration and collaboration in the cloud

Cloud computing services enable organizations and individuals to outsource the management of their data to a service provider in order to save on hardware investments and reduce maintenance costs. Only authorized users are allowed to access the data. Nobody else, including the service provider, should be able to view the data. For instance, a real-estate company that owns a large database of properties wants to allow its paying customers to query for houses according to location. On the other hand, the untrusted service provider should not be able to learn the property locations and, e.g., selling the information to a competitor. To tackle the problem, we propose to transform the location datasets before uploading them to the service provider. The paper develops a spatial transformation that re-distributes the locations in space, and it also proposes a cryptographic-based transformation. The data owner selects the transformation key and shares it with authorized users. Without the key, it is infeasible to reconstruct the original data points from the transformed points. The proposed transformations present distinct trade-offs between query efficiency and data confidentiality. In addition, we describe attack models for studying the security properties of the transformations. Empirical studies demonstrate that the proposed methods are efficient and applicable in practice.

/pdf/enabling-search-services-on-outsourced-private-spatial-data-17v0pn1j74.pdf

Enabling search services on outsourced private spatial data

With the proliferation of mobile computing, the ability to index efficiently the movements of mobile objects becomes important. Objects are typically seen as moving in two-dimensional (x,y) space, which means that their movements across time may be embedded in the three-dimensional (x,y,t) space. Further, the movements are typically represented as trajectories, sequences of connected line segments. In certain cases, movement is restricted, and specifically in this paper, we aim at exploiting that movements occur in transportation networks to reduce the dimensionality of the data. Briefly, the idea is to reduce movements to occur in one spatial dimension. As a consequence, the movement data becomes two-dimensional (x,t). The advantages of considering such lower-dimensional trajectories are the reduced overall size of the data and the lower-dimensional indexing challenge. Since off-the-shelf systems typically do not offer higher-dimensional indexing, this reduction in dimensionality allows us to use such DBMSes to store and index trajectories. Moreover, we argue that, given the right circumstances, indexing these dimensionality-reduced trajectories can be more efficient than using a three-dimensional index. This hypothesis is verified by an experimental study that incorporates trajectories stemming from real and synthetic road networks.

Indexing of network constrained moving objects

Different uses of a road network call for the consideration of different travel costs: in route planning, travel time and distance are typically considered, and green house gas (GHG) emissions are increasingly being considered. Further, travel costs such as travel time and GHG emissions are time-dependent and uncertain. To support such uses, we propose techniques that enable the construction of a multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network. Based on the MTUG, we define stochastic skyline routes that consider multiple costs and time-dependent uncertainty, and we propose efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time. Empirical studies with three road networks in Denmark and a substantial GPS data set offer insight into the design properties of the MTUG and the efficiency of the stochastic skyline routing algorithms.

Stochastic skyline route planning under time-varying uncertainty

The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatiotemporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of such time series offers insight into the underlying system and enables prediction of system behavior. While the techniques presented in the paper apply more generally, we consider the case of transportation systems and aim to predict travel cost from GPS tracking data from probe vehicles. Specifically, each road segment has an associated travel-cost time series, which is derived from GPS data.We use spatio-temporal hidden Markov models (STHMM) to model correlations among different traffic time series. We provide algorithms that are able to learn the parameters of an STHMM while contending with the sparsity, spatio-temporal correlation, and heterogeneity of the time series. Using the resulting STHMM, near future travel costs in the transportation network, e.g., travel time or greenhouse gas emissions, can be inferred, enabling a variety of routing services, e.g., eco-routing. Empirical studies with a substantial GPS data set offer insight into the design properties of the proposed framework and algorithms, demonstrating the effectiveness and efficiency of travel cost inferencing.

/pdf/travel-cost-inference-from-sparse-spatio-temporally-43593vd6m4.pdf

Christian S. Jensen

Papers

Google fusion tables: data management, integration and collaboration in the cloud

Enabling search services on outsourced private spatial data

Indexing of network constrained moving objects

Stochastic skyline route planning under time-varying uncertainty

Travel cost inference from sparse, spatio temporally correlated time series using Markov models