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

Safety and security are top concerns in maritime navigation, particularly as maritime traffic continues to grow and as crew sizes are reduced. The Automatic Identification System (AIS) plays a key role in regard to these concerns. This system, whose objective is in part to identify and locate vessels, transmits location-related information from vessels to ground stations that are part of a so-called Vessel Traffic Service (VTS), thus enabling these to track the movements of the vessels. This paper presents techniques that improve the existing AIS by offering better and guaranteed tracking accuracies at lower communication costs. The techniques employ movement predictions that are shared between vessels and the VTS. Empirical studies with a prototype implementation and real vessel data demonstrate that the techniques are capable of significantly improving the AIS.

/pdf/efficient-vessel-tracking-with-accuracy-guarantees-js0cmccmxc.pdf

Efficient Vessel Tracking with Accuracy Guarantees

The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. L. Liu, M.T. Özsu (Eds.) Encyclopedia of Database Systems

Encyclopedia of Database Systems

Versus Concrete Temporal Query Languages Jan Chomicki, University at Buffalo, USA, http://www.cse.buffalo.edu/~chomicki David Toman, University of Waterloo, Canada, http://www.cs.uwaterloo.ca/~david SYNONYMS historical query languages DEFINITION Temporal query languages are a family of query languages designed to query (and access in general) time-dependent information stored in temporal databases. The languages are commonly defined as extensions of standard query languages for non-temporal databases with temporal features. The additional features reflect the way dependencies of data on time are captured by and represented in the underlying temporal data model. HISTORICAL BACKGROUND Most databases store time-varying information. On the other hand, SQL is often the language of choice for developing applications that utilize the information in these databases. Plain SQL, however, does not seem to provide adequate support for temporal applications. Example. To represent the employment histories of persons, a common relational design would use a schema Employment(FromDate, ToDate, EID, Company), with the intended meaning that a person identified by EID worked for Company continuously from FromDate to ToDate. Note that while the above schema is a standard relational schema, the additional assumption that the values of the attributes FromDate and ToDate represent continuous periods of time is itself not a part of the relational model. Formulating even simple queries over such a schema is non-trivial: for example the query GAPS: “List all persons with gaps in their employment history, together with the gaps” leads to a rather complex formulation in, e.g., SQL over the above schema (this is left as a challenge to readers who consider themselves SQL experts; for a list of appealing, but incorrect solutions, including the reasons why, see [9]). The difficulty arises because a single tuple in the relation is conceptually a compact representation of a set of tuples, each tuple stating that an employment fact was true on a particular day. The tension between the conceptual abstract temporal data model (in the example, the property that employment facts are associated with individual time instants) and the need for an efficient and compact representation of temporal data (in the example, the representation of continuous periods by their start and end instants) has been reflected in the development of numerous temporal data models and temporal query languages [3]. SCIENTIFIC FUNDAMENTALS Temporal query languages are commonly defined using temporal extensions of existing non-temporal query languages, such as relational calculus, relational algebra, or SQL. The temporal extensions can be categorized in two, mostly orthogonal, ways: The choice of the actual temporal values manipulated by the language. This choice is primarily determined by the underlying temporal data model. The model also determines the associated operations on these values. The meaning of temporal queries is then defined in terms of temporal values and operations on them, and their interactions with data (non-temporal) values in a temporal database. The choice of syntactic constructs to manipulate temporal values in the language. This distinction determines whether the temporal values in the language are accessed and manipulated explicitly, in a way similar to other values stored in the database, or whether the access is implicit, based primarily on temporally extending the meaning of constructs that already exist in the underlying non-temporal language (while still using the operations defined by the temporal data model). Additional design considerations relate to compatibility with existing query languages, e.g., the notion of temporal upward compatibility. However, as illustrated above, an additional hurdle stems from the fact that many (early) temporal query languages allowed the users to manipulate a finite underlying representation of temporal databases rather than the actual temporal values/objects in the associated temporal data model. A typical example of this situation would be an approach in which the temporal data model is based on time instants, while the query language introduces interval-valued attributes. Such a discrepancy often leads to a complex and unintuitive semantics of queries. In order to clarify this issue, Chomicki has introduced the notions of abstract and concrete temporal databases and query languages [2]. Intuitively, abstract temporal query languages are defined at the conceptual level of the temporal data model, while their concrete counterparts operate directly on an actual compact encoding of temporal databases. The relationship between abstract and concrete temporal query languages is also implicitly present in the notion of snapshot equivalence [7]. Moreover, Bettini et al. [1] proposed to distinguish between explicit and implicit information in a temporal database. The explicit information is stored in the database and used to derive the implicit information through semantic assumptions. Semantic assumptions about fact persistence play a role similar to mappings between concrete and abstract databases, while other assumptions are used to address time-granularity issues. Abstract Temporal Query Languages Most temporal query languages derived by temporally extending the relational calculus can be classified as abstract temporal query languages. Their semantics is defined in terms of abstract temporal databases which, in turn, are typically defined within the point-stamped temporal data model, in particular without any additional hidden assumptions about the meaning of tuples in instances of temporal relations.Temporal Query Languages Most temporal query languages derived by temporally extending the relational calculus can be classified as abstract temporal query languages. Their semantics is defined in terms of abstract temporal databases which, in turn, are typically defined within the point-stamped temporal data model, in particular without any additional hidden assumptions about the meaning of tuples in instances of temporal relations. Example. The employment histories in an abstract temporal data model would most likely be captured by a simpler schema “Employment(Date, EID, Company)”, with the intended meaning that a person identified by EID was working for Company on a particular Date. While instances of such a schema can be potentially very large (especially when a fine granularity of time is used), formulating queries is now much more natural. Choosing abstract temporal query languages over concrete ones resolves the first design issue: the temporal values used by the former languages are time instants equipped with an appropriate temporal ordering (which is typically a linear order over the instants), and possibly other predicates such as temporal distance. The second design issue—access to temporal values—may be resolved in two different ways, as exemplified by the following two different query languages: •Temporal Relational Calculus (TRC): a two-sorted first-order logic with variables and quantifiers explicitly ranging over the time and data domains (see the entry Temporal Relational Calculus). •First-order Temporal Logic (FOTL): a language with an implicit access to timestamps using temporal connectives (see the entry Temporal Logic in Database Query Languages). Example. The GAPS query is formulated as follows: TRC: ∃t1, t3.t1 < t2 < t3 ∧ ∃c.Employment(t1, x, c) ∧ (¬∃c.Employment(t2, x, c)) ∧ ∃c.Employment(t3, x, c) FOTL: 3∃c.Employment(x, c) ∧ (¬∃c.Employment(x, c)) ∧2∃c.Employment(x, c) Here, the explicit access to temporal values (in TRC) using the variables t1, t2, and t3 can be contrasted with the implicit access (in FOTL) using the temporal operators 3 (read “sometime in the past”) and 2 (read “sometime in the future”). The conjunction in the FOTL query represents an implicit temporal join. The formulation in

Temporal Database Entries for the Springer Encyclopedia of Database Systems

On the Semantics of Now in Temporal Databases

Networked data, notably social network data, often comes with a rich set of annotations, or attributes, such as documents (e.g., tweets) and locations (e.g., check-ins). Community search in such attributed networks has been studied intensively due to its many applications in friends recommendation, event organization, advertising, etc. We study the problem of attribute-constrained co-located community (ACOC) search, which returns a community that satisfies three properties: i) structural cohesiveness: the members in the community are densely connected; ii) spatial co-location: the members are close to each other; and iii) attribute constraint: a set of attributes are covered by the attributes associated with the members. The ACOC problem is shown to be NP-hard. We develop four efficient approximation algorithms with guaranteed error bounds in addition to an exact solution that works on relatively small graphs. Extensive experiments conducted with both real and synthetic data offer insight into the efficiency and effectiveness of the proposed methods, showing that they outperform three adapted state-of-the-art algorithms by an order of magnitude. We also find that the approximation algorithms are much faster than the exact solution and yet offer high accuracy.

Christian S. Jensen

Papers

Efficient Vessel Tracking with Accuracy Guarantees

Encyclopedia of Database Systems

Temporal Database Entries for the Springer Encyclopedia of Database Systems

On the Semantics of Now in Temporal Databases

Efficient Attribute-Constrained Co-Located Community Search