scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
Journal ArticleDOI
TL;DR: An original greedy algorithm to compute t‐wise covering mixed covering arrays with constant space complexity, irrespective of the number of involved parameters and strength of interaction is presented.
Abstract: Combinatorial interaction testing (CIT) is a testing technique that requires covering all t-sized tuples of values out of n parameter attributes or properties modelled after the input parameters or the configuration domain of a system under test. CIT test suites have shown to be very effective in software testing already at pairwise (t = 2) level, and the effectiveness of CIT grows with the tuple width t. Unfortunately, the number of tuples to be tested also does grow. In order to reduce the testing effort, researchers addressed the issue of computing minimal-sized CIT test suites with effective and scalable algorithms. However, still very few generally applicable t-wise covering construction algorithms (and tools) do exist in literature. This paper presents an original greedy algorithm to compute t-wise covering mixed covering arrays with constant space complexity, irrespective of the number of involved parameters and strength of interaction. The proposed algorithm has been implemented in a prototype tool, featuring also support for user constraints over the inputs. Assessment of the tool performance on a set of large, real-world test systems is reported, with results encouraging its adoption in industrial production environments. Copyright © 2011 John Wiley & Sons, Ltd.

34 citations

Patent
26 Jun 2013
TL;DR: In this article, a distributed streaming platform receives a data stream for an application running on a distributed stream platform over a networked cluster of servers, and the software converts the data into a plurality of data tuples structured according to a schema.
Abstract: Software for a distributed streaming platform receives a data stream for an application running on a distributed streaming platform over a networked cluster of servers. The software converts the data into a plurality of data tuples structured according to a schema. And the software repeatedly emits a specified number of the data tuples as a streaming window, which is separated from other streaming windows by a leading control tuple associated with an ordinal identifier for the streaming window and by a trailing control tuple associated with the same ordinal identifier. Then the software emits a checkpointing tuple following the trailing control tuple after a specified number of streaming windows. The checkpointing tuple causes checkpointing of an instance of an operator for the application when the checkpointing tuple is received by the instance.

34 citations

Proceedings ArticleDOI
C. Le Pape1
01 Jan 1995
TL;DR: Each of the three mechanisms is useful, as the time-table and disjunctive mechanisms enable the expression of wider classes of constraints, while edge-finding is in general the most effective in pruning the search space.
Abstract: ILOG SCHEDULE is a C++ library aimed at simplifying the development of industrial scheduling applications. SCHEDULE is based on SOLVER, a generic tool for object-oriented constraint programming. SCHEDULE includes three categories of predefined constraints: temporal constraints, capacity constraints, and resource utilization constraints. Three distinct mechanisms are available to deal with resource utilization constraints. The first mechanism relies on explicit time-tables to maintain information about the variations of resource utilization and resource availability over time. The second mechanism relies on a generic disjunctive constraint which ensures that incompatible activities (for example, activities which require a common resource of capacity 1) cannot overlap in time. The third mechanism, known as edge-finding, considers arbitrary tuples {A/sub 1/...A/sub n/} of activities to prove that some activity A/sub i/ must execute first, or must execute last, among {A/sub 1/...A/sub n/}. The edge-finding mechanism is a priori more CPU-time consuming than the two other mechanisms, but results in the assignment of more precise earliest and latest start and end times to activities. Each of the three mechanisms is useful, as the time-table and disjunctive mechanisms enable the expression of wider classes of constraints, while edge-finding is in general the most effective in pruning the search space.

34 citations

01 Jan 2001
TL;DR: An ordered type is one that has a non-trivial partial order of its values that may be useful in product recommendation and a distinction is drawn at this point between ordered types and unordered types.
Abstract: This paper is about content-based product recommender systems. In product recommendation, a customer is presented with a selection of products from a product catalogue. Content-based approaches (in contradistinction to, e.g., collaborative approaches) select products by matching product descriptions from the catalogue with descriptions of customer preferences and requirements. We will refer to each product description as a case, , and we will refer to the product catalogue as a case base, CB. We assume a set of attributes, , and, for each , a projection function, , which obtains a value for the attribute from the case. For example, price returns the value of case ’s price attribute. This formulation, using projection functions, has the advantage of being agnostic about the actual underlying representation of the cases. They might, for example, be stored as tuples in a relational database, objects in an object-oriented database, or XML documents; all of these can support projection functions. It also allows the possibility of what one might call virtual attributes, where the value returned is not directly stored but is, instead, computed or inferred from what is stored. This is useful, for example, when the case base stores only ‘technical’ data (e.g. a car’s fuel-tank capacity, fuel consumption and top speed) but product selection requires ‘lifestyle’ attributes (e.g. the sportiness of the car). The projection functions for the lifestyle attributes would infer their values from the technical data. The values returned by a projection function will be of some particular type. For example, for a holiday case base, transport might have type train plane car coach ; season might have type Jan Feb Dec ; price might have some suitable set of numbers as its type. To simplify this paper, we will draw a distinction at this point between ordered types and unordered types. We will say that an ordered type is one that has a non-trivial partial order of its values that may be useful in product recommendation. price is an example: since its type is numeric, the values are ordered by the usual ordering of the numbers ( ).

34 citations

Patent
31 Mar 2014
TL;DR: In this article, an extra tuple (referred to below as the AppliedTo tuple) is added to a firewall rule to list the set of enforcement points at which the firewall rule has to be applied (i.e., enforced).
Abstract: Some embodiments of the invention provide a novel method for specifying firewall rules. In some embodiments, the method provides the ability to specify for a particular firewall rule, a set of network nodes (also called a set of enforcement points below) at which the particular firewall should be enforced. To provide this ability, the method of some embodiments adds an extra tuple (referred to below as the AppliedTo tuple) to a firewall rule. This added AppliedTo tuple lists the set of enforcement points at which the firewall rule has to be applied (i.e., enforced).

34 citations


Network Information
Related Topics (5)
Graph (abstract data type)
69.9K papers, 1.2M citations
86% related
Time complexity
36K papers, 879.5K citations
85% related
Server
79.5K papers, 1.4M citations
83% related
Scalability
50.9K papers, 931.6K citations
83% related
Polynomial
52.6K papers, 853.1K citations
81% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023203
2022459
2021210
2020285
2019306
2018266