Općeniti postupak za i ntegracijsko testiranje ETL procedura

In order to attain a certain degree of confidence in the quality of the data in the data warehouse it is necessary to perform a series of tests. There are many components (and aspects) of the data warehouse that can be tested, and in this paper we focus on the ETL procedures. Due to the complexity of ETL process, ETL procedure tests are usually custom written, having a very low level of reusability. In this paper we address this issue and work towards establishing a generic procedure for integration testing of certain aspects of ETL procedures. In this approach, ETL procedures are treated as a black box and are tested by comparing their inputs and outputs – datasets. Datasets from three locations are compared: datasets from the relational source(s), datasets from the staging area and datasets from the data warehouse. Proposed procedure is generic and can be implemented on any data warehouse employing dimensional model and having relational database(s) as a source. Our work pertains only to certain aspects of data quality problems that can be found in DW systems. It provides a basic testing foundation or augments existing data warehouse system’s testing capabilities. We comment on proposed mechanisms both in terms of full reload and incremental loading.Kako bi se ostvarila određena razina povjerenja u kvalitetu podataka potrebno je obaviti niz provjera. Postoje brojne komponente (i aspekti) skladišta podataka koji se mogu testirati. U ovom radu smo se usredotočili na testiranje ETL procedura. S obzirom na složenost sustava skladišta podataka, testovi ETL procedura se pišu posebno za svako skladište podataka i rijetko se mogu ponovo upotrebljavati. Ovdje se obrađuje taj problem i predlaže općenita procedura za integracijsko testiranje određ enih aspekata ETL procedura. Predloženi pristup tretira ETL procedure kao crnu kutiju, te se procedure testiraju tako što se uspoređuju ulazni i izlazni skupovi podataka. Uspoređuju se skupovi podataka s tri lokacije: podaci iz izvorišta podataka, podaci iz konsolidiranog pripremnog područja te podaci iz skladišta podataka. Predložena procedura je općenita i može se primijeniti na bilo koje skladište podatka koje koristi dimenzijski model pri čemu podatke dobavlja iz relacijskih baza podataka. Predložene provjere se odnose samo na određene aspekte problema kvalitete podataka koji se mogu pojaviti u sustavu skladišta podataka, te služe za uspostavljanje osnovnog skupa provjera ili uvećanje mogućnosti provjere postojećih sustava. Predloženi postupak se komentira u kontekstu potpunog i inkrementalnog učitavanja podataka u skladište podataka

Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines

A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Regional Data Archiving and Management for Northeast Illinois

This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the regional transportation agencies, from both technical and business perspectives, about building such a comprehensive transportation information system. Several implementation alternatives are identified and analyzed. This research is carried out in three phases. In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect information on all data elements that they store, including the format, system, and granularity. Their perception of a data archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the hardware, software, labor, and resource requirements. We also identify possible revenue opportunities. A few implementation options for the archive system are summarized in this report; namely: 1. System hosted by a partnering agency 2. System contracted to a university 3. System contracted to a national laboratory 4. System outsourced to a service provider The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe

Fast Data in the Era of Big Data: Twitter's Real-Time Related Query Suggestion Architecture

We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time "twist": after significant breaking news events, we aim to provide relevant results within minutes. This paper provides a case study illustrating the challenges of real-time data processing in the era of "big data". We tell the story of how our system was built twice: our first implementation was built on a typical Hadoop-based analytics stack, but was later replaced because it did not meet the latency requirements necessary to generate meaningful real-time results. The second implementation, which is the system deployed in production, is a custom in-memory processing engine specifically designed for the task. This experience taught us that the current typical usage of Hadoop as a "big data" platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on data analytics platforms that can handle "big" as well as "fast" data