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Aqua data studio package body12/12/2023 It supports every major database vendor in the market, as well as all operating systems. The data visualization system is categorized as a productivity software solution and is designed and created by AquaFold, along with other innovative software applications and tools tailored for software professionals.Īqua Data Studio is fully capable of dealing with various types of databases, be it relational databases, NoSQL or cloud-hosted databases. ![]() It provides end-to-end solutions for Windows systems.Īt its core, Aqua Data Studio is a database-integrated development environment that is utilized by different entities and professionals, including database developers, analysts and administrators in order to develop, access, analyze and manage relevant data. Many of these features are being added to the SQL Standard.Aqua Data Studio is a robust and feature-rich data visualization platform tailored for small and midsize enterprises (SMEs) and agencies. This paper (1) explains the cube and roll-up operators, (2) shows how they fit in SQL, (3) explains how users can define new aggregate functions for cubes, and (4) discusses efficient techniques to compute the cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. The set of points forms an N-dimensional cube. The aggregate of a particular set of attribute values is a point in this space. The cube operator treats each of the N aggregation attributes as a dimension of N-space. Consequently, the cube operator can be imbedded in more complex non-procedural data analysis programs. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. This paper defines that operator, called the data cube or simply cube. Applications need the N-dimensional generalization of these operators. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional aggregates. Finally we present experimental results over real world scenarios to demonstrate the feasibility of the approach.ĭata analysis applications typically aggregate data across many dimensions looking for anomalies or unusual patterns. ![]() ![]() Based on this representation, reasoning capabilities are supported by a rule-based engine. This makes the approach easily extensible and independent from the specific knowledge representation language. ![]() The approach is based on the notion of construct, that represents a concept of the domain of interest. In this paper we present a framework which provides a flexible and persistent layer relying on a novel storage model that guarantees good scalability and performance of query evaluation. Despite RDF has the advantage of being general and simple, it cannot be used as a storage model as it is, since it can be easily shown that even simple management operations involve serious performance limitations. In this context, the Resource Description Framework (RDF) has been conceived to provide an easy way to represent any kind of data and metadata, according to a lightweight model and syntaxes for serialization (RDF/XML, N3, etc.). The Semantic Web is gaining increasing interest to fulfill the need of sharing, retrieving, and reusing information.
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