无线传感器网络中n向流连接技术的比较评价

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Boubekeur Djail
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引用次数: 0

摘要

在无线传感器网络中,使用关系查询访问传感器数据。连接查询通常用于从存储在无线传感器网络不同部分的多个表中检索数据。然而,这样的查询需要大量的能量。许多研究都试图降低查询能耗。然而,大多数建议的技术都解决了在静态表之间执行的二进制连接。很少考虑数据流之间的n路连接。使用数据流的连接查询持续工作并且需要越来越多的能量,这就是为什么涉及多个表的n-way连接消耗如此多的能量。因此,挑战在于减少能量耗散。此外,有必要确定n-way连接的适当执行顺序。n路连接可能实现的数量随着表的数量呈指数增长。本文对数据流之间n路连接的一些有趣的方法进行了评价。所比较的方法有Stern等人的extern-join、Sens-join以及NSLJ (N-way Stream Local Join)和NSLSJ (N-way Stream Local Semi-Join)两种技术。根据几个参数进行比较,以确定哪个用例适合每种技术。NSLSJ最适合具有低连接选择因子的连接查询,而外部连接更适合具有非常高选择因子的查询。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative Evaluation of Techniques for n-way Stream Joins in Wireless Sensor Networks
In wireless sensor networks, sensor data are accessed using relational queries. Join queries are commonly used to retrieve the data from multiple tables stored in different parts of a wireless sensor network. However, such queries require large amounts of energy. Many studies have intended to reduce query energy consumption. However, most of the proposed techniques addressed binary joins which are performed between static tables. N-way joins between data streams were rarely considered. Join queries using data streams work continuously and require increasing energy, which is why n-way joins involving several tables consume so much energy. Thus, the challenge lies in reducing energy dissipation. Additionally, it is necessary to determine the appropriate execution order for an n-way join. The number of possible implementations of an n-way join grows exponentially with the tables’ number. In this paper, interesting approaches for n-way joins between streams of data are evaluated. The methods that have been compared are extern-join, Sens-join of Stern et al, and the two techniques NSLJ (N-way Stream Local Join) and NSLSJ (N-way Stream Local Semi-Join). Comparisons are conducted according to several parameters to determine which use case is appropriate for each technique. NSLSJ works best for join queries with low join selectivity factors, while extern-join is more suitable for queries with very high selectivity factors.
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
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