OLAP analysis of user energy consumption based on multitemporal distribution characteristics

Traditional data warehousing technologies and On-Line Analytical Processing (OLAP) are unable to analyze textual data. Moreover, as OLAP queries of a decision-maker are generally related to a context, contextual information must be taken into account during the exploitation of data warehouses. Thus, we propose a contextual text cube model denoted CXT-Cube which considers several contextual factors during the OLAP analysis in order to better consider the contextual information associated with textual data. CXT-Cube is characterized by several contextual dimensions, each one related to a contextual factor. In addition, we extend our aggregation OLAP operator for textual data ORank (OLAP-Rank) to consider all the contextual factors defined in our CXT-Cube model. To validate our model, we perform an experimental study and the preliminary results show the importance of our approach for integrating textual data into a data warehouse and improving the decision-making.

Defining and structuring management views is a fundamental task of the specification of data marts and reporting systems. Today such systems are based on data warehouses. A data warehouse stores materialized views on business processes to support the management’s information requirements (cf. Inmon 1996; Inmon, Hackathorn 1994; Inmon et al. 1997). It is located on a central layer of an idealized layer oriented architecture connecting online transaction processing (OLTP) systems and components enabling online analytical processing (OLAP) (cf. Becker, Holten 1998; Chaudhuri, Dayal 1997). The latter components are intended to support navigations adequate for management users through so called multi dimensional information spaces. OLTP systems directly support the business processes and are the sources of data used by OLAP systems. Typically, the integration of OLTP systems and the data warehouse is based on tools performing so called extraction, transformation and loading tasks (ETL) (cf. Inmon 1996; Widom 1995)

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

Ever increasing DRAM capacity has fostered the development of in-memory databases (IMDB). The massive performance improvements provided by IMDBs have enabled transactions and analytics on the same database. In other words, the integration of OLTP (on-line transactional processing) and OLAP (on-line analytical processing) systems is becoming a general trend. However, conventional DRAM-based main memory is optimized for row-oriented accesses generated by OLTP workloads in row-based databases. OLAP queries scanning on specified columns cause so-called strided accesses and result in poor memory performance. Since memory access latency dominates in IMDB processing time, it can degrade overall performance significantly. To overcome this problem, we propose a dual-addressable memory architecture based on non-volatile memory, called RC-NVM, to support both row-oriented and column-oriented accesses. We first present circuit-level analysis to prove that such a dual-addressable architecture is only practical with RC-NVM rather than DRAM technology. Then, we rethink the addressing schemes, data layouts, cache synonym, and coherence issues of RC-NVM in architectural level to make it applicable for IMDBs. Finally, we propose a group caching technique that combines the IMDB knowledge with the memory architecture to further optimize the system. Experimental results show that the memory access performance can be improved up to 14.5X with only 15% area overhead.

Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a Big Data scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multi-hypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive.

This paper elaborates on the justification and need for integration of OLTP (online transaction processing) and OLAP (online analytical processing) data environments for the purpose of real time data analysis, and reviews potential implementation methods and architectures for integration of these disparate environments. A body of relevant literature is reviewed to answer a central question on the efficacy of OLTP and OLAP integrated environments, and the central question is made tractable by its decomposition to the essential questions that must be answered regarding the feasibility of various methods and architectures for integrating data architectures. Additional coverage is given to proposed architectures and methods for real time data warehouses in the context of OLTP and OLAP data integration. This qualitative study uses an interpretivist epistemology to reach conclusions about the literature's treatment of the topic and the justification and need for data integration to accomplish real time data analysis in the business enterprise.

Hybrid Transactional-Analytical Processing (HTAP) databases face significant challenges in CPU resource allocation due to the conflicting requirements of Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads. Traditional static scheduling approaches fail to adapt to dynamic workload patterns, leading to suboptimal performance and resource utilization inefficiencies. The diverse characteristics of transactional and analytical queries require sophisticated scheduling strategies that can balance latency-sensitive transaction processing with throughput-oriented analytical operations.This study proposes a Hierarchical Deep Reinforcement Learning (HDRL) framework for adaptive CPU scheduling in HTAP database systems. The framework employs a two-level architecture where a high-level agent manages workload prioritization between OLTP and OLAP components, while low-level agents optimize resource allocation within each processing type. Deep Q-Networks (DQN) and Actor-Critic algorithms enable dynamic adaptation to changing workload patterns and system conditions.Experimental evaluation using industry-standard benchmarks demonstrates that the proposed framework achieves 34% improvement in overall system throughput while reducing OLTP query latency by 28% compared to traditional scheduling methods. The hierarchical approach successfully balances competing workload demands and adapts to varying system conditions, resulting in enhanced resource utilization efficiency and improved Quality of Service (QoS) guarantees across both transactional and analytical processing requirements.

SUMMARYNetwork traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. TheNetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposedNetCubetraffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.

Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on meta-data which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schema-based, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.

On-line analytical processing provides multidimensional data analysis, through extensive computation based on aggregation, along many dimensions and hierarchies. To accelerate query-response time, pre-computed results are often stored for later retrieval. This adds a prohibitive storage overhead when applied to the whole set of aggregates. In this paper we describe a novel approach which provides the means for the efficient selection, computation and storage of multidimensional aggregates. The approach identifies redundant aggregates, by inspection, thus allowing only distinct aggregates to be computed and stored. We propose extensions to relational theory and also present new algorithms for implementing the approach, providing a solution which is both scalable and low in complexity. The experiments were conducted using real and synthetic datasets and demonstrate that significant savings in computation time and storage space can be achieved when redundant aggregates are eliminated. Savings have also been shown to increase as dimensionality increases. Finally, the implications of this work affect the indexing and maintenance of views and the user interface.KeywordsData CubeDerivative RelationInput RelationDimension NumberOLAP QueryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this world, each and every activity is enriched with lot of information. Business and other organization needs information for better decision making. Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Some of the functions of business intelligence technologies are reporting, Online Analytical Processing, Online Transaction processing, data mining, process mining, complex event processing, business performance management, benchmarking and text mining. The applications of business intelligence includes E-commerce recommender system, approval of bank loan, credit/debit card fraud detection etc., In order to obtain business intelligence from large dataset there many techniques are available in data mining such as characterization, discrimination, frequent itemset mining, outlier analysis, cluster analysis and so on. In this proposed algorithm frequent itemset mining and clustering algorithm is used to extract the information from the dataset in order to make the decision making process more efficient and to improve the business intelligence.

In an e-commerce system, the database server performance is crucial. Dynamic caching is often used to reduce the load on the database server, which reduces the need for scalability within the server itself. A good understanding of the workload characteristics of the database server in an e-commerce environment is important to the design, tuning, and capacity planning. Using dynamic caching can dramatically alter the characteristics of requests that the database server receives, changing the dominant operations, even the workload type. The two major database workload classifications are online analytical processing and online transaction processing. Allocations for resources such as main memory can be very different, depending on the workload type. We investigate the e-commerce activity profiles resulting from dynamic caching. We find that dynamic caching does tend to alter the behavior towards transaction processing.

In the past, classical databases basically came in two flavors: optimized for write access (OLTP, online transaction processing) or read access (OLAP, online analytic processing). Typical data mining tasks, however, involve preprocessing, feature extraction, model training and cross-validation which cannot fully be categorized as either flavor. SAP’s in-memory database HANA stores all data cache-aware in main memory, allowing for rapid transactional and analytical access. While in common three-tier architectures (database, application server, client), computationally intensive applications run at the application server layer and data is loaded into the main memory of application servers, enterprise applications developed for or moved to HANA are more tightly integrated with the database. The main principle of application development for HANA is to execute data-intensive computations in the database close to the raw data in order to prevent expensive data movement. This shift in application design poses new challenges to the application developer: in order to utilize HANA efficiently, he has to think differently about how to design his application.We’ll address these challenges, discuss a real-world data analysis scenario and present some open questions in this area.

13 - Other Modeling Aspects and Trends

Hybrid storage systems are widely used in big data fields to balance system performance and cost. However, due to a poor understanding of the characteristics of database block requests, past studies in this area cannot fully utilize the performance gain from emerging storage devices. This study presents a hybrid storage database system, called HeuristicDB, which uses an emerging non-volatile memory (NVM) block device as an extension of the database buffer pool. To consider the unique performance behaviors of NVM block devices and the block-level characteristics of database requests, a set of heuristic rules that associate database (block) requests with the appropriate quality of service for the purpose of caching priority are proposed. Using online analytical processing (OLAP) and online transactional processing (OLTP) benchmarks, both trace-based examination and system implementation on MySQL are carried out to evaluate the effectiveness of the proposed design. The experimental results indicate that HeuristicDB provides up to 75% higher performance and migrates 18X fewer data between storage and the NVM block device than existing systems.