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.

Mr-SBC: A Multi-relational Naïve Bayes Classifier

Abstract: In this paper we propose an extension of the naive Bayes classification method to the multi-relational setting. In this setting, training data are stored in several tables related by foreign key constraints and each example is represented by a set of related tuples rather than a single row as in the classical data mining setting. This work is characterized by three aspects. First, an integrated approach in the computation of the posterior probabilities for each class that make use of first order classification rules. Second, the applicability to both discrete and continuous attributes by means a supervised discretization. Third, the consideration of knowledge on the data model embedded in the database schema during the generation of classification rules. The proposed method has been implemented in the new system Mr-SBC, which is tightly integrated with a relational DBMS. Testing has been performed on two datasets and four benchmark tasks. Results on predictive accuracy and efficiency are in favour of Mr-SBC for the most complex tasks.

Symmetric Weighted First-Order Model Counting

Abstract: The FO Model Counting problem (FOMC) is the following: given a sentence Φ in FO and a number n, compute the number of models of Φ over a domain of size n; the Weighted variant (WFOMC) generalizes the problem by associating a weight to each tuple and defining the weight of a model to be the product of weights of its tuples. In this paper we study the complexity of the symmetric WFOMC, where all tuples of a given relation have the same weight. Our motivation comes from an important application, inference in Knowledge Bases with soft constraints, like Markov Logic Networks, but the problem is also of independent theoretical interest. We study both the data complexity, and the combined complexity of FOMC and WFOMC. For the data complexity we prove the existence of an FO3 formula for which FOMC is #P1-complete, and the existence of a Conjunctive Query for which WFOMC is #P1-complete. We also prove that all γ-acyclic queries have polynomial time data complexity. For the combined complexity, we prove that, for every fragment FOk, k ≥ 2, the combined complexity of FOMC (or WFOMC) is #P-complete.

Exploitation of uniqueness properties using a 1-tuple condition for the optimization of SQL queries

Abstract: The present invention optimizes SQL queries by exploiting uniqueness properties. In identifying whether the generalized 1-tuple condition exists, the query is first analyzed to determine whether any columns referenced in a predicate of the query are bound. According to the present invention, columns may be bound to constant values or correlated columns or columns that are already bound. The bound columns, if any, are then analyzed to determine whether any of the bound columns comprise a key for its associated table. If these conditions exist, then the query satisfies the 1-tuple condition, in that it returns at most one tuple. Once the generalized 1-tuple condition has been identified to exist for the query, important query transformations can be performed for optimization purposes. These query transformations comprise the transformation of scalar subqueries into joins, or the elimination of distinctiveness requirements (i.e., DISTINCT keywords) from SELECT clauses.

MABAC method for multiple attribute group decision making under picture 2-tuple linguistic environment

Abstract: In this article, we extend multi-attributive border approximation area comparison (MABAC) approach to the multiple attribute group decision making with picture 2-tuple linguistic numbers. We review the concept of picture 2-tuple linguistic sets and introduce its corresponding score function, accuracy function, and operational laws. In addition, we propose two aggregation operators of picture 2-tuple linguistic numbers and then develop a method by combining traditional MABAC model with the overall picture 2-tuple linguistic evaluation information. Our proposed method is increasingly accurate and valid even when the conflicting attributes are considered. We also provide a numerical instance for assessing and selecting the renewable energy power generation project to demonstrate the efficacy of our novel model. Finally, we compare our proposed approach with other traditional operators to further show its benefits.

Safe Data Parallelism for General Streaming

Abstract: Streaming applications process possibly infinite streams of data and often have both high throughput and low latency requirements. They are comprised of operator graphs that produce and consume data tuples. General streaming applications use stateful, selective, and user-defined operators. The stream programming model naturally exposes task and pipeline parallelism, enabling it to exploit parallel systems of all kinds, including large clusters. However, data parallelism must either be manually introduced by programmers, or extracted as an optimization by compilers. Previous data parallel optimizations did not apply to selective, stateful and user-defined operators. This article presents a compiler and runtime system that automatically extracts data parallelism for general stream processing. Data-parallelization is safe if the transformed program has the same semantics as the original sequential version. The compiler forms parallel regions while considering operator selectivity, state, partitioning, and graph dependencies. The distributed runtime system ensures that tuples always exit parallel regions in the same order they would without data parallelism, using the most efficient strategy as identified by the compiler. Our experiments using 100 cores across 14 machines show linear scalability for parallel regions that are computation-bound, and near linear scalability when tuples are shuffled across parallel regions.