DynaMat

Materialized aggregate views represent a set of redundant entities in a data warehouse that are frequently used to accelerate On-Line Analytical Processing (OLAP). Due to the complex structure of the data warehouse and the different profiles of the users who submit queries, there is need for tools that will automate and ease the view selection and management processes. In this article we present DynaMat, a system that manages dynamic collections of materialized aggregate views in a data warehouse. At query time, DynaMat utilizes a dedicated disk space for storing computed aggregates that are further engaged for answering new queries. Queries are executed independently or can be bundled within a multiquery expression. In the latter case, we present an execution mechanism that exploits dependencies among the queries and the materialized set to further optimize their execution. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We show how to derive an efficient update plan with respect to the available maintenance window, the different update policies for the views and the dependencies that exist among them.

Materialized view selection is one of the crucial decisions in designing a data warehouse for optimal efficiency. Static selection of views may materialize certain views that are not beneficial as the data and usage trends change over time. On the contrary, dynamic selection of views works better only for queries demanding a high degree of aggregation. These facts point to the need for a technique that combines the improved response time of the static approach and the automated tuning capability of the dynamic approach. In this article, we propose a hybrid approach for the selection of materialized views. The idea is to partition the collection of all views into a static and dynamic set such that views selected for materialization from the static set are persistent over multiple query (and maintenance) windows, whereas views selected from the dynamic set can be queried and/or replaced on the fly. Highly aggregated views are selected on the fly based on the query access patterns of users, whereas the more detailed static set of views plays a significant role in the efficient maintenance of the dynamic set of views and in answering certain detailed view queries. We prove that our proposed strategy satisfies the monotonicity requirements, which is essential in order for the greedy heuristic to deliver competitive solutions. Experimental results show that our approach outperforms Dynamat, a well-known dynamic view management system that is known to outperform optimal static view selection.

A data warehouse is designed for answering analytical queries. These queries are usually long, complex and ad hoc in nature and their response time is high when processed against an exponentially growing data warehouse. Materialising views has been found to be an effective tool for reducing this response time. All views cannot be materialised on account of resource constraints. Further, optimal view selection is shown to be an NP-complete problem. This necessitates selection of an appropriate set of views, from amongst all possible views that reduce the query response time. Most of the view selection algorithms are greedy or evolutionary. In this paper, a differential evolution view selection algorithm (DEVSA) that selects the Top-K views from a multi-dimensional lattice is proposed. Further, it is shown that DEVSA when compared to the greedy, genetic and memetic-based view selection algorithms selects comparatively better quality views for higher dimensional datasets.

A data warehouse is a central repository of historical data designed primarily to support analytical processing. These analytical queries are exploratory, long and complex in nature. Further, the rapid and continuous growth in the size of data warehouse increases the response times of such queries. Query response times need to be reduced in order to speedup decision making. This problem, being an NP-Complete problem, can be appropriately dealt with by using swarm intelligence techniques. One such technique, i.e. the set-based particle swarm optimization (SPSO), has been proposed to address this problem. Accordingly, a SPSO based view selection algorithm (SPSOVSA), which selects the Top-K views from a multidimensional lattice, is proposed. Experimental based comparison of SPSOVSA with the most fundamental view selection algorithm shows that SPSOVSA is able to select comparatively better quality Top-K views for materialization. The materialization of these selected views would improve the performance of analytical queries and lead to efficient decision making.

Materialized view selection under the maintenance time constraint

In order to facilitate efficient query processing, the information contained in data warehouses is typically stored as a set of materialized views. Deciding which views to materialize represent a challenge in order to minimize view maintenance and query processing costs. Some existing approaches are applicable only for small problems, which are far from reality. In this paper we introduce a new approach for materialized view selection using Parallel Simulated Annealing (PSA) that selects views from an input Multiple View Processing Plan (MVPP). With PSA, we are able to perform view selection on MVPPs having hundreds of queries and thousands of views. Also, in our experimental study we show that our method provides a significant improvement in the quality of the obtained set of materialized views over existing heuristic and sequential simulated annealing algorithms.

With the emergence of modern database technologies, the concept of multi-granular data warehousing, which stores data at multiple levels of granularity has become important. In order to store multigranular data, the existing data warehousing schemas, such as star and snowflake are unable to provide support, for the reason that they are designed to store data at a single level of granularity. In this paper, we present a multi-dimensional and multi-granular data warehousing schema (MMDW). MMDW is based on Relational OLAP (ROLAP) and uses a RDBMS to manage new and old (aggregated) data by means of an extended star schema. In contrast to traditional ROLAP, MMDW also allows pre-computation and storage of data at multiple levels of granularity. Furthermore, MMDW is evaluated based on a real world case study and results show that MMDW performs well in terms of aggregation query speed, aggregation query complexity as well as storage used and compares favorably with standard star schema.

The volume of information contained in the databases is constantly increasing, the spatio-temporal data collected by the sensor network form a set of thematic, integrated, nonvolatile and historical data organized for use in helping to decision. Typically, this process is performed using temporal, spatial, and time-space queries. This in turn increases the execution time of decision requests. To optimize the load of queries, it is essential to choose the multiple selection of similar structures. In the literature, several optimization techniques have been identified: materialized views, indexes, fragmentation, planning and buffer management. These techniques do not consider the change in the request load and the data change. In this article, we propose an optimal so-called dynamic selection based on the use of indexes and materialized views. a solution is said to be optimal when it answers all the requests with a minimum response time. Our approach allows to optimize the execution of the requests and to adapt an optimal solution to the modifications of data in the base one applies the algorithm of backpack.

On-Line Analytical Processing (OLAP) tools are frequently used in business, science and health to extract useful knowledgefrom massive databases. An important and hard optimization problem in OLAP data warehouses is the view selection problem, consisting of selecting a set of aggregate views of the data for speeding up future query processing. A common variant of the view selection problem addressed in the literature minimizes the sum of maintenance cost and query time on the view set. Converting what is inherently an optimization problem with multiple conflicting objectives into one with a single objective ignores the need and value of a variety of solutions offering various levels of trade-off between the objectives. We apply two non-elitist multiobjective evolutionary algorithms (MOEAs) to view selection under a size constraint. Our emphasis is to determine the suitability of the combination of MOEAs with constraint handling to the view selection problem, compared to a widely used greedy algorithm. We observe that the evolutionary process mimics that of the greedy in terms of the convergence process in the population. The MOEAs are competitive with the greedy on a variety of problem instances, often finding solutions dominating it in a reasonable amount of time.

In this paper, we propose a new method for 2D/3D object indexing and retrieval. The principle consists of an automatic selection of optimal views by using an incremental algorithm based on pivot selection techniques for proximity searching in metric spaces. The selected views are afterward described by four well-established descriptors from the MPEG-7 standard, namely: the color structure descriptor (CSD), the scalable color descriptor (SCD), the edge histogram descriptor (EHD) and the color layout descriptor (CLD). We present our results on two databases: The Amsterdam Library of Images (ALOI-1000), consisting of 72,000 color images of views, and the Columbia Object Image Library (COIL-100), consisting of 7200 color images of views. The results prove the performance of the developed method and its superiority over the k-means algorithm and the automatic selection of optimal views proposed by Mokhtarian et al.

For speeding up query processing on Big Data, frequent sub-queries or views may be materialized such that the query processing cost is minimized with optimum cost of maintaining the materialized views and/or queries. Materializing frequent sub-queries and views means that resultant data set of the views reside in the memory of one or more nodes in the cluster, so that it reduces the MapReduce cost, submission and scheduling cost of Distributed File System jobs for query processing. We have defined materialized views as resultant data of frequent sub-queries and aggregation functions of a set of Big Data warehousing queries that are saved for enhancing query performance. The problem is defined as a multi-objective optimization problem for minimizing the total query processing MapReduce cost, MapReduce cost for maintaining the materialized views and the number of views selected for materializing with maximized total size of the views selected. We applied Differential Evolution algorithm and NSGA-II to study their performances for developing a recommendation system for selecting views for materializing in Big Data warehousing.

A Data Warehouse (DW) can be abstractly seen as a set of materialized views defined over a set of remote data sources. A DW is intended to satisfy a set of queries. The views materialized in a DWrelate to each other in a complex manner, through common subexpressions, in order to guarantee high query performance and low view maintenance cost. DWs are time varying. As time passes new materialized views are added in order to satisfy new queries or for performance reasons while old queries are dropped. The evolution of a DWcan result in a redundant set of materialized views.In this paper we address the problem of detecting redundant views in a given DW view selection, that is, views that can be removed from the DW without negatively affecting the query evaluation or the view maintenance process. Using an AND/OR dag representation for multiple queries and views, we first provide a method for detecting materialized views that are not needed in the process of propagating source relation changes to the DW. Then, we use this method to detect materialized views that are redundant. As a side effect, our approach shows how source relation changes can be propagated to the DW materialized views by exploiting common subexpressions between views and by using other materialized views that are not affected by these changes.

Nowadays, materialized views are increasingly being supported by a variety of commercial DBMS to speed up query response time. This technique is also very useful in dataware-housing for optimizing OLAP queries. However, most existing view selection methods are of static nature. Moreover, none of these methods have considered the problem of dematerializing the previously materialized views. This paper deals with the problem of dynamic view selection and the pending issue of removing materialized views in order to replace the less beneficial views with more beneficial views. More precisely, we have designed and implemented a view selection method, including a polynomial algorithm, to decide which views to materialize according to statistic metadata.

View materialization is a commonly used technique in many data-intensive systems to improve the query performance. Increasing need for large-scale data processing has led to investigating the view selection problem in distributed complex scenarios where a set of cooperating computer nodes may share data and issue numerous queries. In our work, the view selection and data placement problem is studied given a limited amount of resources e.g. storage space capacity per computer node and maximum view maintenance cost. We also consider the IO and CPU costs for each computer node as well as the network bandwidth. To address this problem, we have proposed a constraint programming approach which is based on constraint reasoning to tackle problems that aim to satisfy a set of constraints. Then, we have designed a set of efficient heuristics that result in a drastic reduction in the solution space so that the problem becomes solvable for complex scenarios consisting of realistically large numbers of sites, queries and views. Our experimental study shows that our approach performs consistently better compared to a practical approach designed for large-scale distributed environments which uses a genetic algorithm to compute which view has to be materialized at what computer node.

Accurate hand-eye calibration is crucial for robotics and computer vision tasks, such as robotic manipulation and 3D object recognition, as it directly affects operational precision and visual perception. However, existing methods rely solely on point clouds and neglect geometric features like plane normals, leading to suboptimal spatial alignment. Additionally, the lack of an intelligent view selection strategy worsens initial alignment errors, causing cumulative inaccuracies and degraded performance. We propose HCNV, a novel framework that combines optimized point cloud alignment with intelligent view selection to overcome these limitations. Our method uses plane normals and a hybrid genetic algorithm to refine the point cloud transformation matrix, significantly improving spatial alignment accuracy. Furthermore, the intelligent view selection strategy enhances point cloud matching and optimizes view coverage, increasing robustness across various conditions. Experimental results show that HCNV improves calibration accuracy by 20% and reduces computational time by 30% compared to state-of-the-art methods, demonstrating its effectiveness and practicality in real-world applications.