Tuple

About: Tuple is a research topic. Over the lifetime, 6513 publications have been published within this topic receiving 146057 citations. The topic is also known as: tuple & ordered tuplet.

CrossMine: efficient classification across multiple database relations

Abstract: Most of today's structured data is stored in relational data- bases. Such a database consists of multiple relations that are linked together conceptually via entity-relationship links in the design of relational database schemas. Multi-relational classification can be widely used in many disciplines including financial decision making and medical research. However, most classification approaches only work on single “flat” data relations. It is usually difficult to convert multiple relations into a single flat relation without either introducing huge “universal relation” or losing essential information. Previous works using Inductive Logic Programming approaches (recently also known as Relational Mining) have proven effective with high accuracy in multi-relational classification. Unfortunately, they fail to achieve high scalability w.r.t. the number of relations in databases because they repeatedly join different relations to search for good literals. In this paper we propose CrossMine, an efficient and scalable approach for multi-relational classification. CrossMine employs tuple ID propagation, a novel method for virtually joining relations, which enables flexible and efficient search among multiple relations. CrossMine also uses aggregated information to provide essential statistics for classification. A selective sampling method is used to achieve high scalability w.r.t. the number of tuples in the databases. Our comprehensive experiments on both real and synthetic databases demonstrate the high scalability and accuracy of CrossMine.

Multiple active L3 gateways for logical networks

Abstract: Some embodiments provide a method for a network controller in a network control system that manages a plurality of logical networks. The method receives a specification of a logical network that comprises a logical router with a logical port that connects to an external network. The method selects several host machines to host a L3 gateway that implements the connection to the external network for the logical router from a set of host machines designated for hosting logical routers. The method generates data tuples for provisioning a set of managed forwarding elements that implement the logical network to send data packets that require processing by the L3 gateway to the selected host machines. The data tuples specify for the managed forwarding elements to distribute the data packets across the selected host machines.

Outerjoins as disjunctions

Abstract: The outerjoin operator is currently available in the query language of several major DBMSs, and it is included in the proposed SQL2 standard draft. However, “associativity problems” of the operator have been pointed out since its introduction. In this paper we propose a shift in the intuition behind outerjoin: Instead of computing the join while also preserving its arguments, outerjoin delivers tuples that come either from the join or from the arguments. Queries with joins and outerjoins deliver tuples that come from one out of several joins, where a single relation is a trivial join. An advantage of this view is that, in contrast to preservation, disjunction is commutative and associative, which is a significant property for intuition, formalisms, and generation of execution plans.Based on a disjunctive normal form, we show that some data merging queries cannot be evaluated by means of binary outerjoins, and give alternative procedures to evaluate those queries. We also explore several evaluation strategies for outerjoin queries, including the use of semijoin programs to reduce base relations.

Bootstrapping for Numerical Open IE

Abstract: We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase. BONIE uses bootstrapping to learn the specific dependency patterns that express numerical relations in a sentence. BONIE’s novelty lies in task-specific customizations, such as inferring implicit relations, which are clear due to context such as units (for e.g., ‘square kilometers’ suggests area, even if the word ‘area’ is missing in the sentence). BONIE obtains 1.5x yield and 15 point precision gain on numerical facts over a state-of-the-art Open IE system.

Aggregation of imprecise and uncertain information in databases

Abstract: Information stored in a database is often subject to uncertainty and imprecision. Probability theory provides a well-known and well understood way of representing uncertainty and may thus be used to provide a mechanism for storing uncertain information in a database. We consider the problem of aggregation using an imprecise probability data model that allows us to represent imprecision by partial probabilities and uncertainty using probability distributions. Most work to date has concentrated on providing functionality for extending the relational algebra with a view to executing traditional queries on uncertain or imprecise data. However, for imprecise and uncertain data, we often require aggregation operators that provide information on patterns in the data. Thus, while traditional query processing is tuple-driven, processing of uncertain data is often attribute-driven where we use aggregation operators to discover attribute properties. The aggregation operator that we define uses the Kullback-Leibler information divergence between the aggregated probability distribution and the individual tuple values to provide a probability distribution for the domain values of an attribute or group of attributes. The provision of such aggregation operators is a central requirement in furnishing a database with the capability to perform the operations necessary for knowledge discovery in databases.