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

Mobile services is emerging as an important application area for spatio-temporal database management technologies. Service users are often constrained to a spatial network, e.g., a road network, through which points of interest, termed data points, are accessible. Queries that implement services will often concern data points of some specific type, e.g., Thai restaurants or art museums. As a result, the relatively few data points are relevant to a query in comparison to the number of network edges, meaning that queries, e.g., k nearest-neighbor queries, must access large portions of the network.

Existing query processing techniques pre-compute distances between data points and network vertices for improving the performance. However, precomputation becomes problematic when the network or data points must be updated, possibly concurrently with the querying; and if the data points are moving, the existing techniques are inapplicable. In addition, multiple pre-computed structures must be maintained--one for each type of data point. We propose a versatile pre-computation approach for spatial network data. This approach uses a grid for pre-computing a simplified network. The above-mentioned shortcomings are avoided by making the pre-computed data independent of the data points. Empirical performance studies show that the structure is competitive with respect to the existing, more specialized techniques.

/pdf/s-grid-a-versatile-approach-to-efficient-query-processing-in-40jqh3untm.pdf

S-GRID: a versatile approach to efficient query processing in spatial networks

In adaptive query processing, query plans are improved at ru ntime by means of feedback. In the very flexible approach based on so-called eddies, query executio n is treated as a process of routing tuples to the query operators that combine to compute a query. This mak es it possible to alter query plans at the granularity of tuples. Further, the complex task of searchi ng the query plan space for a suitable plan now resides in the routing policies used. These policies must ad apt to the changing execution environment and must converge at a near-optimal plan when the environmen t stabilizes. This paper advances adaptive query processing in two respec ts. First, it proposes a general framework for the routing problem that may serve the same role for a daptive query processing as does the framework of search in query plan space for conventional que ry processing. It thus offers an improved foundation for research in adaptive query processing. The f ramework leverages reinforcement learning theory and formalizes a tuple routing policy as a mapping fro m a state space to an action space, capturing query semantics as well as routing constraints. In effect, t he framework transforms query optimization from a search problem in query plan space to an unsupervised l earning problem with quantitative rewards that is tightly coupled with the query execution. The f ramework covers selection queries as well as joins that use all proposed join execution mechanisms (SH Js, SteMs, STAIRs). Second, in addition to showing how existing routing policies can fit into the fram ework, the paper demonstrates new routing policies that build on advances in reinforcement learning. By means of empirical studies, it is shown that the proposed policies embody the desired adaptivity an d co vergence characteristics, and that they are capable of clearly outperforming existing policies.

A Reinforcement Learning Approach for Adaptive Query Processing

Vehicle routing is an important service that is used by both private individuals and commercial enterprises. Drivers may have different contexts that are characterized by different routing preferences. For example, during different times of day or weather conditions, drivers may make different routing decisions such as preferring or avoiding highways. The increasing availability of vehicle trajectory data yields an increasingly rich data foundation for context-aware, preference-based vehicle routing. We aim to improve routing quality by providing new, efficient routing techniques that identify and take contexts and their preferences into account. In particular, we first provide means of learning contexts and their preferences, and we apply these to enhance routing quality while ensuring efficiency. Our solution encompasses an off-line phase that exploits a contextual preference tensor to learn the relationships between contexts and routing preferences. Given a particular context for which trajectories exist, we learn a routing preference. Then, we transfer learned preferences from contexts with trajectories to similar contexts without trajectories. In the on-line phase, given a context, we identify the corresponding routing preference and use it for routing. To achieve efficiency, we propose preference-based contraction hierarchies that are capable of speeding up both off-line learning and on-line routing. Empirical studies with vehicle trajectory data offer insight into the properties of proposed solution, indicating that it is capable of improving quality and is efficient.

https://link.springer.com/content/pdf/10.1007/s00778-020-00608-7.pdf

Context-aware, preference-based vehicle routing

The increased deployment of sensors and data communication networks yields data management workloads with update loads that are intense, skewed, and highly bursty. Query loads resulting from location-based services are expected to exhibit similar characteristics. In such environments, index structures can easily become performance bottlenecks. We address the need for indexing that is adaptive to the workload characteristics, called workload-aware, in order to cover the space in between maintaining an accurate index, and having no index at all. Our proposal, QU-Trade, extends R-tree type indexing and achieves workload-awareness by controlling the underlying index's filtering quality. QU-Trade safely drops index updates, increasing the overlap in the index when the workload is update-intensive, and it restores the filtering capabilities of the index when the workload becomes query-intensive. This is done in a non-uniform way in space so that the quality of the index remains high in frequently queried regions, while it deteriorates in frequently updated regions. The adaptation occurs online, without the need for a learning phase. We apply QU-Trade to the R-tree and the TPR-tree, and we offer analytical and empirical studies. In the presence of substantial workload skew, QU-Trade can achieve index update costs close to zero and can also achieve virtually the same query cost as the underlying index.

/pdf/workload-aware-indexing-of-continuously-moving-objects-58pi6vruvv.pdf

Workload-aware indexing of continuously moving objects

Ubiquitous and accurate indoor positioning represents a key capability of an infrastructure that enables indoor location-based services. At the same time, such positioning has yet to be achieved. Much research uses commercial, off-the-shelf 802.11 (Wi-Fi) hardware for indoor positioning. In particular, the dominant fingerprinting technique uses a database (called a radio map) of manually collected Wi-Fi signal strengths and is able to achieve positioning accuracies that enable a wide range of location-based services. However, a major weakness of fingerprinting occurs when changes occur in the indoor environment that cause the signal propagation patterns and thus signal strength to change. Under such circumstances, a radio map collected at one time is unable to offer accurate positioning at all times. We propose a data-centric approach to achieving accurate positioning in changing environments. Unlike previous work, our approach does not require the deployment of special sensors that capture current signal strength phenomena, but rather lends itself towards ubiquitous indoor positioning. An empirical comparison of our proposals against conventional, static radio maps demonstrates very significant improvements in positioning accuracy in changing environments.

Christian S. Jensen

Papers

S-GRID: a versatile approach to efficient query processing in spatial networks

A Reinforcement Learning Approach for Adaptive Query Processing

Context-aware, preference-based vehicle routing

Workload-aware indexing of continuously moving objects

Algorithmic strategies for adapting to environmental changes in 802.11 location fingerprinting