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SPATIAL OLAP QUERY ENGINE: PROCESSING AGGREGATE QUERIES ON SPATIAL OLAP DATA

SPATIAL OLAP QUERY ENGINE: PROCESSING AGGREGATE QUERIES ON SPATIAL OLAP DATA

Unsurprisingly, a vast amount of data accumulated into data warehouses are also spatial data. However, traditional data warehouses and OLAP systems have not been able to process spatial data very well. Recently, researchers have begun to focus on developing specialized OLAP techniques to handle spatial data efficiently, by noting radical differences between spatial data and non-spatial data. We can call this discipline of research spatial OLAP. Indeed, spatial OLAP refers to the confluence of two technologies that are on the rise. They are spatial data management and OLAP technologies. In geographic information system (GIS) application areas, there has been considerable interest in employing the potential of spatial OLAP (e.g., [3] and [8]), although they do not define spatial OLAP in exactly same ways as we, database researchers, do.
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Processing and Preparation of OLAP data cube

Processing and Preparation of OLAP data cube

Ker v času informacij postajajo informacije vedno bolj obsežne in tudi njihova količina neizmerno hitro raste (angl. »Big Data«), nastajajo nove platforme in rešitve na področju podatkovnih skladišč. Ena izmed takih je odprtokodna platforma Hadoop, ki je zelo učinkovita rešitev za zelo velike količine podatkov. Jedro Hadoop- a, podjetja Apache, je sestavljeno iz dela za shranjevanje, ki je znan kot Hadoop Distributed File System (HDF), in procesorskega dela, imenovanega MapReduce. Hadoop razdeli datoteke v velike bloke in jih distribuira preko vozlišč v gruči. S tem pridobimo na hitrosti in na zmožnostih shranjevanja velikih količin podatkov.
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OLAP. Data Mining Decision

OLAP. Data Mining Decision

For this sake, we rest on methods from the fields of databases, data mi- ning, information retrieval and service technologies at all levels of the ware- housing process: data integration (ETL), multidimensional modeling and OLAP. The main topics we address are text data warehouses, social OLAP and personalization.

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Data-Warehouse-, Data-Mining- und OLAP-Technologien

Data-Warehouse-, Data-Mining- und OLAP-Technologien

Analysis Transformation OLAP Data Mining Information Visualization Nomenclature Business Resource Object Model Relational Record Multidimensional XML Foundation Information Business Types Data Expression Keys and Indexes Mapping Type Deployment Software Object Model

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AN OVERVIEW OF DATA WAREHOUSING AND OLAP TECHNOLOGY FOR AN EDUCATIONAL INSTITUTION

AN OVERVIEW OF DATA WAREHOUSING AND OLAP TECHNOLOGY FOR AN EDUCATIONAL INSTITUTION

Boon KeongSeah, MIMOS Technology Park Malaysia, Malaysia (2014) IEEE this paper describes government bodies are enhancing their decision making capabilities using data warehouse. For government bodies, data warehouse provides a means by enabling policy making to be formulated much easier based on available data such as survey-based services data. In this paper we present a survey-based service data with the design and implementation of a Data Warehouse framework for data mining and business intelligence reporting. In the design of the data warehouse, developed a multidimensional Data Model for the creation of multiple data marts and design of an ETL process for populating the data marts from the data source. The paper introduced by Payame Noor University(2014), the many advantages that this system provides for training centers, there are still many problems in the use of these systems and many questions remain unanswered feasibility study of factors influencing students' success and success in attracting students and decision making in order to increase the efficiency of resource allocation issues that are causing challenges for managers and other professionals in the areas of teaching and learning. Business intelligence strategies and analysis of online tools can be used in order to overcome these problems. In this paper, research on business intelligence, analytical databases, and how to investigate online processing system (OLAP) data analysis will be educational environments. The paper introduced by ManjunathT.N, Ravindra S Hegadi. The proposed model evaluates the data quality of decision databases and evaluates the model at different dimensions like accuracy derivation integrity, consistency, timeliness, completeness, validity, precision and interpretability, on various data sets after migration. The proposed data quality assessment model evaluates the data at different dimensions to give confidence for the end users to rely on their businesses [3].
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Overview of Multidimensional Data Model - OLAP

Overview of Multidimensional Data Model - OLAP

During the past decade, the multidimensional data model emerged for use when the objective is to analyse data rather than to perform online transactions. We extend the OLAP data model to representambiguity of dataand on-line analytical processing (OLAP), an essential element of decision support, which has increasingly become an emphasis of our new database industry. We relate natural query properties and use them to shed light on different query semantics. There is much more work required on decision support on database technology compared to traditional on- line transaction processing applications. This paper provides an overview of OLAP technology, with a prominence on their new requirements.
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Data Warehousing and OLAP Technology

Data Warehousing and OLAP Technology

11.1. Materialization of data cube ......................................... 40 11.2. Cube Operation ............................................................. 41 11.3. Cube Computation Methods ......................................... 43 11.4. Multi-way Array Aggregation for Cube Computation Error! Bookmark not defined. 11.5. Indexing OLAP Data: Bitmap Index ............................ 44

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A Data Mining-Based OLAP Aggregation of. Complex Data: Application on XML Documents

A Data Mining-Based OLAP Aggregation of. Complex Data: Application on XML Documents

adopted as a pre-processing step in the knowledge discovery process. In the same context, Maedche, Hotho and Wiese (2000) combine databases with classical data mining systems by using OLAP engine as interface and treat telecommunication data. In this interface, OLAP tools create a target data set to generate new hypotheses by applying data mining methods. Tjioe and Taniar (2005) propose a method for mining association rules in data warehouses. Based on the multidimensional data organization, this method is capable of extracting associations from multiple dimensions at multiple levels of abstraction by focusing on measurements of summarized data. In order to do this, the authors propose to prepare multidimensional data for the mining process according to four algorithms: VAvg, HAvg, WMAvg, and ModusFilter. These algorithms prune all rows in the fact table which have less than the average quantity and provide an “initialized table”. The latter table is used next for mining both on non-hybrid (non-repeatable predicate) and hybrid (repeatable predicate) association rules. Fu (2005) proposes an algorithm, called CubeDT, for constructing decision tree classifiers based on data cubes. This algorithm works on statistic trees which are
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A Comprehensive Study on Data Warehouse, OLAP and OLTP Technology

A Comprehensive Study on Data Warehouse, OLAP and OLTP Technology

Abstract: Data warehouse is an integrated, subject oriented, time variant and non-volatile collection of data used for decision making. It is a type of database for decision making which is separately maintained from the operational database. Data warehouse provides a very important tool OLAP (online analytical processing) for more intensive decision support. The main characteristics of OLAP are: advanced database support, analysis of multidimensional data, support client-server architecture and easy to use end user interface. So, data warehouse and OLAP are very important elements for decision making and knowledge discovery process. In this paper, we discussed about data warehouse, OLAP and OLTP and also described data warehouse model and some back end tools.
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Clinical Decision Support Using OLAP With Data Mining

Clinical Decision Support Using OLAP With Data Mining

Jonathan et al. [18] used data mining to identify factors contributing to prenatal outcomes as well as quality and cost of prenatal care. Bansal et al. [19] used recurrent neural networks to predict sales forecast for a medical company. Margaret et al. [20] explored the use of artificial neural networks to predict length of hospital stay and its effectiveness in resource allocation. There was very little discussion on the use of data mining in decision support. Hedger [21] used an advanced data mining system to evaluate healthcare utilization costs for GTE’s employees and dependents. He also explored the use of IDIS (Intelligence Ware) to discover factors leading to success or failure in back surgeries.
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OLAP in the Data Warehouse

OLAP in the Data Warehouse

One-Line Analytical Processing (OLAP) is a category of software technology that enables analysts, managers, and executives to gain insight into data through fast, consistent, interactive access in a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.

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Keywords : Business Intelligence, Data Warehouse, Data Mining, Cube, OLAP.

Keywords : Business Intelligence, Data Warehouse, Data Mining, Cube, OLAP.

In terms of output, the business intelligence gives features that provide quick and accurate information. By using ETL (Extract Transform Load), we can collect data from a variety of applications that report is no longer just one application. From the side of flexibility of OLAP analysis, business intelligence provides information quickly in accordance with the data we want. Drill-down method also allows the user to more in-depth analysis, for example, from the level of the year, into the quarter level, month level and beyond.
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OLAP AND DATA WAREHOUSE BY W. H. Inmon

OLAP AND DATA WAREHOUSE BY W. H. Inmon

Figure 16 shows the different types of data found in the OLAP environment. Permanent detailed data is data that comes from the organizationally structured level and is regularly and normally needed in OLAP processing. Permanent detailed data will be detailed from the standpoint of the department that owns the OLAP platform. In actuality, the OLAP permanent detailed data may well be summarized as it passes from the organizationally structured level of the data warehouse into the OLAP environment. In that respect, what is detailed in any one instance of the OLAP environment may be summarized from the perspective of the corporate DSS analyst. Referring back to Figure 1, the data warehouse architecture supports maintaining he appropriate level of detail and summarization to support the informational requirements of the entire organization, as well as the different functional requirements of different departments within the
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A Call Detail Records Data Mart: Data Modelling and OLAP Analysis

A Call Detail Records Data Mart: Data Modelling and OLAP Analysis

Abstract. In order to succeed in the market, telecommunications companies are not competing solely on price. They have to expand their services based on their knowledge of customers’ needs gained through the use of call detail records (CDR) and customer demographics. All the data should be stored together in the CDR data mart. The paper covers the topic of its design and development in detail and especially focuses on the conceptual/logical/physical trilogy. Some other design problems are also discussed. An important area is the problem involving time. This is why the implication of time in data warehousing is carefully considered. The CDR data mart provides the platform for Online Analytical Processing (OLAP) analysis. As it is presented in this paper, an OLAP system can help the telecommunications company to get better insight into its customers’ behaviour and improve its marketing campaigns and pricing strategies.
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HaoLap: a Hadoop based OLAP system for big data

HaoLap: a Hadoop based OLAP system for big data

In recent years, facing information explosion, industry and academia have adopted distributed file system and MapReduce programming model to address new challenges the big data has brought. Based on these tech- nologies, this paper presents HaoLap (Hadoop based oLap), an OLAP (OnLine Analytical Processing) system for big data. Drawing on the experience of Multidimensional OLAP (MOLAP), HaoLap adopts the specified multi- dimensional model to map the dimensions and the measures; the dimension coding and traverse algorithm to achieve the roll up operation on dimension hierarchy; the partition and linearization algorithm to store dimensions and measures; the chunk selection algorithm to optimize OLAP performance; and MapReduce to execute OLAP. The paper illustrates the key techniques of HaoLap including system architecture, dimension definition, dimension coding and traversing, partition, data storage, OLAP and data loading algorithm. We evaluated HaoLap on a real application and compared it with Hive, HadoopDB, HBaseLattice, and Olap4Cloud. The experiment results show that HaoLap boost the efficiency of data loading, and has a great advantage in the OLAP performance of the data set size and query complexity, and meanwhile HaoLap also completely support dimension operations.
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OLAP on Complex Data : Visualization Operator Based on Correspondence Analysis

OLAP on Complex Data : Visualization Operator Based on Correspondence Analysis

As said above, warehousing and online analytical processes must be modified in the case of complex objects. In this paper, we focus on the visualization of complex objects. The problem of storing and modeling complex objects is dis- cussed in other articles [6, 5]. The purpose of online analysis is to (1) aggregate many data to summarize the information they contain; (2) display the informa- tion according to different dimensions (3) navigate through data to explore them. OLAP operators are well-defined for classic data. But they are inadequate when data are complex. The use of other techniques, for example data mining, may be promising. Combining data mining methods with OLAP tools is an interesting solution for enhancing the ability of OLAP to analyze complex objects. We have already suggested extending OLAP capabilities with complex object exploration and clustering [2, 3].
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A STRATEGY FOR COST EFFICIENT DISTRIBUTED DATA STORAGE FOR IN-MEMORY OLAP

A STRATEGY FOR COST EFFICIENT DISTRIBUTED DATA STORAGE FOR IN-MEMORY OLAP

metadata server, which is activated only in case of failure. Multiple metadata servers ensure performance and reliability. (2) Metadata is separated from data and handled in separate processes. Lustre [Braam 2002] provides an asymmetrical system architecture with one metadata server and multiple object storage servers. There is an exclusive cluster node for the metadata server process in GFS/HDFS. As a rule, the metadata server also handles, e.g., time stamps, locking, load balancing, and data distribution in the cluster. The GFS master contains the mapping table for all chunks with information about their size, location, and mapping to the appropriate file in the landscape, and initiates chunk rebalancing if necessary. In Ceph, the metadata server adapts its behavior to the current workload dynamically by metadata replication across multiple servers [Weil 2006a]. Namespaces in Farsite are stored separately in a hierarchical tree structure that allows different namespace roots. Several servers controlling namespace roots are summarized in a directory group. As Farsite is a decentral network file system for a huge number of heterogeneous hosts [Adya 2002], it has to handle an insecure infrastructure, i.e., hosts that may crash without warning or leak information to third parties. To ensure metadata integrity in a directory group, it makes use of the Byzantine Agreement protocol. The metadata of a directory group with R D group members is considered as valid if not more than (R D – 1)/3 members are erroneous. The extremely wide-area storage system OceanStore [Rhea 2003] uses cryptographic approaches to ensure data integrity in an insecure environment.
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Medicine Decision Support System Using OLAP with Data Warehousing

Medicine Decision Support System Using OLAP with Data Warehousing

R2. The integrated model improves information visualization. It discovers overall trends that are likely to be missed by using OLAP or data warehousing alone. Fig. 9 (a) shows that of all the city in Jordan, most number of patients diagnosed with anemia. Barring this, the best anemia specialists are from King Abdullah Hospital. (Fig. 9 (b). It provides a more comprehensive analysis and facilitates decision- making by allocating physicians to under- represented geographical areas. It allows the quality of physicians in under represented areas to be improved.
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Data Warehouse and Business Intelligence: Comparative Analysis of Olap tools

Data Warehouse and Business Intelligence: Comparative Analysis of Olap tools

“a copy of transaction data specifically structured for query and analysis.”. A data warehouse contains massive amounts of highly detailed, time-series data used for decision support. Specialized software extracts data from operational databases, then summarizes, reconciles, and manipulates it for business use (Breslin, 2004). A data warehouse is generally understood as an integrated and time-varying collection of data primarily used in strategic decision making by means of online analytical processing (OLAP) techniques. An organization must choose a set of data warehouse design and maintenance tools from among scores of software tools commercially available in market. Mimno (1997) stated that selecting data warehousing product is complex because there are not any single vendor that provides a fully integrated set of products that support all components of a data warehouse. As a result, data warehouses are typically built using products from multiple vendors. It is difficult to decide on the combination of products that are the best for an organization.
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Keyword search on large-scale data: from relational and graph data to OLAP infrastructure

Keyword search on large-scale data: from relational and graph data to OLAP infrastructure

document, a tuple in a relation table, or a node in a labeled graph). However, in the multiple- entity answer model, the keywords in a query can be distributed into several entities which are closely related. In the keyword search problem, another core task is providing related queries as sugges- tions to assist users’ search process. With the assistance of keyword search, both experienced users and inexperienced users can easily retrieve valuable information from large-scale struc- tured, semi-structured, and unstructured data. However, in most cases, a keyword query typically is very short, which on average only contains 2 or 3 keywords [77]. Such short keyword queries may not be able to precisely capture users’ information needs. As a result, keyword queries in practice are often ambiguous. To improve the effectiveness of keyword search, most popular commercial Web search engines currently provide a useful service called query suggestion. By conjecturing a user’s real search intent, the Web search engines can recommend a set of related queries which may better reflect the user’s true information need.
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