Index-assisted hierarchical computations in main-memory RDBMS

Brunel, Robert; May, Norman; Kemper, Alfons

doi:10.14778/2994509.2994524

“…Compared to SQL-based relational database management system (RDBMS), which uses tables to save data, Indexed DB is an object-oriented database. Indexed DB allows for objects to be saved/retrieved with keys [16]. Web applications can only access and operate saved structured data through the API provided by Indexed DB.…”

Section: Introductionmentioning

confidence: 99%

A New Security Model for Web Browser Local Storage

Al-Rousan¹,

Al-Shargabi²,

Abualese³

2019

IJACSA

3

0

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In recent years, the web browser has taken over many roles of the traditional operating system, such as acting as a host platform for web applications. Web browser storage, where the web applications can save data locally was one of the new functionalities added in HTML5. However, web functionality has increased significantly since HTML5 was introduced. As web functionality increased, so did the threats facing web users. One of the most prevalent threats was the user's privacy violations. This study examines the existing security issues related to the usage of web browser storage and proposes a new model to secure the data saved in the browser's storage. The model was designed and implemented as a web browser extension to secure the saved data. The model was experimentally demonstrated and the result was evaluated.

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“…In the case of union, when a measure depends on a subset of the dimensions of a fact table, the propagation rule requires a uniqueness test (to check the LFD X 𝑑 ↦ → 𝐴) on the result of the union. Since the uniqueness test involves hierarchical dimension attributes only, it can be performed efficiently using specific data structures used to represent fact tables in main-memory (see [6,7]).…”

mentioning

confidence: 99%

Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

Simon¹,

Amann²,

Liu³

et al. 2021

Preprint

0

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We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and BI tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table, which aggregation functions correctly aggregate the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes which are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules which transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.

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Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

Simon¹,

Amann²,

Liu³

et al. 2023

J. Data and Information Quality

0

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We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and BI tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table, which aggregation functions correctly aggregate the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes which are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules which transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.

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Index-assisted hierarchical computations in main-memory RDBMS

Cited by 3 publications

References 23 publications

A New Security Model for Web Browser Local Storage

A New Security Model for Web Browser Local Storage

Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

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