Dynamic set may refer to: A set (abstract data type) that supports insertion and/or deletion of elements; Dynaset- A data structure frequently used in relational database access

In computer science, a set is an abstract data type that can store unique values, without any particular order. It is a computer implementation of the mathematical concept of a finite set. Unlike most other collection types, rather than retrieving a specific element from a set, one typically tests a value for membership in a set.

2021年10月21日 · There are no dynamic sets. Set theory has nothing to say about time. It just defines what is, and what isn't, a set. Typically, the elements of a set are fixed, and are based on how the set is defined. The rules governing what is, and what isn't, a set are very strict. If the answer to "Is 1 1 an element of this set?"

discuss a packing algorithm for (dynamic) set intersection, and in Section 3 we show how the packing algorithm for set intersection can be used to speed up triangle listing.

Dynamic sets are most often used as controlling indices in assignments or equation definitions and as the conditional set in a dollar-controlled indexed operation. We will first show how assignments are used to change set membership in dynamic sets.

Such sets are called dynamic; Dynamic set objects may have an identifying key field that uniquely identifies the object.

2015年3月20日 · A Dynamic Set is an Abstract Data Type that can support the following operations: (1) search for an item to determine whether the item exists in the set; (2) insert a new item into the set;

2014年2月27日 · Describe how to use a bit vector to Represent a Dynamic Set of Distinct Elements with no Satellite Data. Dictionary Operations Should Run in O (1) Time. Solution (I get the result from Rip’s blog, The original text is here for ease of reference.):

2019年6月29日 · 许多应用程序中, 数据对象可以表示为集(set)。例如, 在视频点播和社交网络服务中,用户数据由一组分别已观看的电影和一组用户 (朋友) 组成, 它们可用于推荐和信息提取。因此, 集相似自联接问题得到了广泛的研究。现有的研究假定集是静态的,但在上述应用程序 ...

2019年6月29日 · Dynamic set kNN self-join 的主要瓶颈是集合之间的相似性计算. 为了消除瓶颈并更快的更新相似度 (即在 O (1) 时间内),我们设计一中算法,我们为每个集合设定指标index, 指标为集合s和s’的差 (|s\s’|). 更进一步,我们提供了一个cost model 去决定 index的阈值, 即再确保正确性的前提下更有效率的更新kNN结果. 我们针对这个问题使用的算法是: LI-DSN-Join (Local-Index-based dynamic set kNN self-join) s. R L. ∀ s ′ ∈ s. R L, s ∈ s ′. s i. 算法分为两步: s i. D 和 s i. R L. ∀ s …