Analytical queries defined on data warehouses are complex and use several join operations that are very costly, especially when run on very large data volumes. To improve response times, data warehouse administrators casually use indexing techniques. This task is nevertheless complex and fastidious. In this paper, we present an automatic, dynamic index selection method for data warehouses that is based on incremental frequent itemset mining from a given query workload. The main advantage of this approach is that it helps update the set of selected indexes when workload evolves instead of recreating it from scratch. Preliminary experimental results illustrate the efficiency of this approach, both in terms of performance enhancement and overhead.
The aim of this article is to present an overview of the major families of state-of-the-art index and materialized view selection methods, and to discuss the issues and future trends in data warehouse performance optimization. We particularly focus on data mining-based heuristics we developed to reduce the selection problem complexity and target the most pertinent candidate indexes and materialized views.
With the development of decision systems and specially data warehouses, the visibility of the data warehouse design before its creation has become essential, and that because of data warehouse importance as considered as the unique data source giving meaning to the decision. In a decision system the proper functioning of a data warehouse resides in the smooth running of the middleware tools ETC step one hand, and the restitution step through the data mining, reporting solutions, dashboards... etc other. The large volume of data that passes through these stages require an optimal design for a h…
Decision systems deal with a large volume of data stored in new databases called data warehouses. Data warehouses are typically modeled by a star schema that conventionally presents a central fact table and a set of dimension tables. The corresponding queries for this type of model are therefore very complex. In order to reduce the cost of executing complex queries, which contain very expensive joins, the solution envisaged would be to guarantee a good physical design of the data warehouses. Binary join indexes are very suitable to reduce the cost of executing these joins. In this work, we pro…
XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native-XML database management systems (DBMSs) currently bear limited performances and it is necessary to research for ways to optimize them. In this chapter, we present two such techniques. First, we propose a join index that is specifically adapted to the multidimensional architecture of XML warehouses. It eliminates join operations while preserving the information contained in the original warehouse. Second, we present a strategy for selecting XML materialized views by cluster…
Presently, large enterprises rely on database systems to manage their data and information. These databases are useful for conducting daily business transactions. However, the tight competition in the marketplace has led to the concept of data mining in which data are analyzed to derive effective business strategies and discover better ways in carrying out business. In order to perform data mining, regular databases must be converted into what so called informational databases also known as data warehouse. This paper presents a design model for building data warehouse for a typical university …
The article discusses the approach for evaluating the innovation index of the products and technologies. The evaluation results can be used to create a warehouse of the object descriptions with significant innovation potential. The model of innovation index computation is based on the concepts of novelty, relevance, and implementability of the object. Formal definitions of these indicators are given and a methodology for their calculation are described. The fuzzy methods to coprocess (incomplete) data from numerous sources and to obtain probabilistic innovation assessments are used. The experi…
This paper presents a family of algorithms for fast subset filtering within ordered sets of integers representing composite keys. Applications include significant acceleration of (ad-hoc) analytic queries against a data warehouse without any additional indexing. The algorithms work for point, range and set restrictions on multiple attributes, in any combination, and are inherently multidimensional. The main idea consists in intelligent combination of sequential crawling with jumps over large portions of irrelevant keys. The way to combine them is adaptive to characteristics of the underlying d…
Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse. Techniques to accelerate the execution of filter expressions include clustered indexes, specialized sort orders (e.g., Z-order), multi-dimensional indexes, and, for high selectivity queries, secondary indexes. However, these schemes are hard to tune and their performance is inconsistent. Recent work on learned multi-dimensional indexes has introduced the idea of automatically optimizing an index for a particular dataset and workload. However, the performance of that work suffers in the p…
We propose a new approach of NoSQL database index selection. For different workloads, we select different indexes and their different parameters to optimize the database performance. The approach builds a deep reinforcement learning model to select an optimal index for a given fixed workload and adapts to a changing workload. Experimental results show that, Deep Reinforcement Learning Index Selection Approach (DRLISA) has improved performance to varying degrees according to traditional single index structures.
The index selection problem (ISP) is an important problem for accelerating the execution of relational queries, and it has received a lot of attention as a combinatorial knapsack problem in the past. Various solutions to this very hard problem have been provided. In contrast to existing literature, we change the underlying assumptions of the problem definition: we adapt the problem for systems that store relations in memory, and use complex specification languages, e.g., Datalog. In our framework, we decompose complex queries into primitive searches that select tuples in a relation for which a…
Finding the shortest paths in road network is an important query in our life nowadays, and various index structures are constructed to speed up the query answering. However, these indexes can hardly work in real-life scenario because the traffic condition changes dynamically, which makes the pathfinding slower than in the static environment. In order to speed up path query answering in the dynamic road network, we propose a framework to support these indexes. Firstly, we view the dynamic graph as a series of static snapshots. After that, we propose two kinds of methods to select the typical sn…
Configuration spaces for computer systems can be challenging for traditional and automatic tuning strategies. Injecting task-specific knowledge into the tuner for a task may allow for more efficient exploration of candidate configurations. We apply this idea to the task of index set selection to accelerate database workloads. Index set selection has been amenable to recent applications of vanilla deep RL, but real deployments remain out of reach. In this paper, we explore how learning index selection can be enhanced with task-specific inductive biases, specifically by encoding these inductive …
Distributed Search Engine Architecture (DSEA) hosts numerous independent topic-specific search engines and selects a subset of the databases to search within the architecture. The objective of this approach is to reduce the amount of space needed to perform a search by querying only a subset of the total data available. In order to manipulate data across many databases, it is most efficient to identify a smaller subset of databases that would be most likely to return the data of specific interest that can then be examined in greater detail. The selection index has been most commonly used as a …
