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.

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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

A number of applications in areas such as logistics, cargo delivery, and collective transport involve the management of fleets of vehicles that are expected to travel along known routes according to fixed schedules. Due to road construction, accidents, and other unanticipated conditions, the vehicles deviate from their schedules. At the same time, there is a need for the infrastructure surrounding the vehicles to continually know the actual status of the vehicles. For example, anticipated arrival times of buses may have to be displayed at bus stops. It is a fundamental challenge to maintain this type of knowledge with minimal cost. This paper characterizes the problem of real-time vehicle tracking using wireless communication, and of predicting the future status of the vehicles when their movements are restricted to given routes and when they follow schedules with the best effort. The paper discusses challenges related to tracking, to the prediction of future travel times, and to historical data analysis. It also suggests approaches to addressing the challenges

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Challenges in the Tracking and Prediction of Scheduled-Vehicle Journeys

Range queries in metric spaces have applications in many areas such as multimedia retrieval, computational biology, and location-based services, where metric uncertain data exists in different forms, resulting from equipment limitations, high-throughput sequencing technologies, privacy preservation, or others. In this paper, we represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed accordingly in order to support probabilistic range queries w.r.t. a wide range of uncertain data types and similarity metrics. Both index structures use a small set of effective pivots chosen based on a newly defined criterion, and employ the B+-tree(s) as the underlying index. By design, they are easy to be integrated into any existing DBMS. In addition, we present efficient metric probabilistic range query algorithms, which utilize the validation and pruning techniques based on our derived probability lower and upper bounds. Extensive experiments with both real and synthetic data sets demonstrate that, compared against existing state-of-the-art indexes for metric uncertain data, the UPB-tree and UPB-forest incur much lower construction costs, consume smaller storage spaces, and can support more efficient metric probabilistic range queries.

Indexing Metric Uncertain Data for Range Queries

Origin-destination (OD) matrices are often used in urban planning, where a city is partitioned into regions and an element (i, j) in an OD matrix records the cost (e.g., travel time, fuel consumption, or travel speed) from region i to region j. In this paper, we partition a day into multiple intervals, e.g., 96 15-min intervals and each interval is associated with an OD matrix which represents the costs in the interval; and we consider sparse and stochastic OD matrices, where the elements represent stochastic but not deterministic costs and some elements are missing due to lack of data between two regions. We solve the sparse, stochastic OD matrix forecasting problem. Given a sequence of historical OD matrices that are sparse, we aim at predicting future OD matrices with no empty elements. We propose a generic learning framework to solve the problem by dealing with sparse matrices via matrix factorization and two graph convolutional neural networks and capturing temporal dynamics via recurrent neural network. Empirical studies using two taxi datasets from different countries verify the effectiveness of the proposed framework.

Recurrent Multi-Graph Neural Networks for Travel Cost Prediction.

We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in applications such as trip planning and recommendation. To process the TSR query, a set of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

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Searching Trajectories by Regions of Interest

A spatial join computes all pairs of spatial objects in two data sets satisfying a distance constraint. An increasing demand in applications ranging from human brain analysis to transportation data analysis motivates studies on designing new in-memory spatial join algorithms. Among recent proposals, the following six algorithms can efficiently perform in-memory spatial joins: Size Separation Spatial Join (S3), Spatial Grid Hash join (SGrid), TOUCH, Partition Based Spatial-Merge Join (PBSM), Plane-Sweep Join (PS), and Nested-Loop Join (NL). This paper addresses the need for studies of aspects that may influence the performance of spatial join algorithms. In particular, given two datasets, A and B, the following aspects may affect performance: the datasets being real or synthetic data, the distributions with respect to density and location of the datasets, and the order of performing the spatial join (A 1 B or B 1 A). To study the effects on performance of these aspects, we implement the six spatial join algorithms in a single framework and conduct extensive experiments. The findings show that the data being real or synthetic, the data distribution, and the join order can influence substantially the performance of the algorithms. We present detailed findings that offer insight into different facets of each algorithm and that enable comparison across algorithms and datasets. Furthermore, we provide advice on choosing among the spatial join algorithms based on the empirical evaluation.

Christian S. Jensen

Papers

Challenges in the Tracking and Prediction of Scheduled-Vehicle Journeys

Indexing Metric Uncertain Data for Range Queries

Recurrent Multi-Graph Neural Networks for Travel Cost Prediction.

Searching Trajectories by Regions of Interest

In-Memory Spatial Join: The Data Matters!