Efficient stowage planning is essential to enhance operational performance in container terminals. Our research focuses on the stowage planning of dangerous goods containers (SPDGC), a variant of the problem incorporating various real-world constraints inspired by the literature. Given the complexity of this problem, we propose a hybrid approach that combines the use of the advanced detection algorithm Faster R-CNN to classify and group containers based on their types. This classification is then utilized as the initial state for the Coronavirus metaheuristic algorithm, aiming to reorganize bays inside the container ship and determine the optimal location for each container. Our paper presents the computational results and comparative studies, demonstrating the effectiveness of our approach in addressing the complex container stowage problem.

Analytic processing systems have been traditionally designed to optimize time performance, leaving energy as a secondary aspect. More recently, during the past decade, there has been a growing interest in addressing the energy efficiency of analytics and in particular query processing (QP), our focus in this article. Numerous solutions, spanning both software and hardware approaches, have been proposed in database systems, but they have important limitations: (i) They were designed for old QP architectures, (ii) they do not consider emerging QP AI trends, such as learned query plans and hybrid QP, and (iii) they lack a well-defined framework with clear steps, which can be applied in a modern data science ecosystem. With such reasons in mind, we introduce a general framework that will help researchers and industry practitioners in addressing energy-efficiency challenges. Our framework, named ${SATM}^{2}V$, integrates five major steps: (1) assessing public sentiment and alerting analysts on the real impact of data science on decarbonization, (2) conducting “under the hood” energy consumption audits to identify energy-hungry components, (3) turning on/off and tuning parameters of components to understand their contribution to energy savings. (4) developing models and measurement techniques for quantifying energy consumption, (5) developing and executing tactics for energy saving. We then turn our attention to database systems and identify relational join processing as a representative energy consumption example. QP becomes particularly difficult when dealing with queries involving multiple join operations since join ordering is known to be an NP-hard problem, whose optimal solution remains an open problem. We apply our solution framework to the specific case of hybrid QPs, studying the impact of various join ordering optimization techniques on energy efficiency. Extensive experiments are conducted using the well-known Join Order Benchmark dataset to evaluate the effectiveness and tradeoffs of several query optimization techniques on time and energy consumption.

Climate change is a pressing global issue primarily driven by excessive energy consumption, with the building sector being a major contributor. In response to this challenge, Nearly Zero-Energy Buildings (NZEBs) have emerged as a promising solution. These buildings are designed to generate as much energy as they consume by leveraging energy-efficient technologies and renewable energy sources. However, achieving the NZEB goal relies on accurately predicting a building’s future energy usage. This precision is crucial for efficiently managing energy production, consumption, storage, and distribution within NZEBs. The current Artificial Intelligence (AI) solutions face significant shortcomings that extend beyond mere performance issues. These include critical aspects such as the lack of transparency in gathered data and predicted results, oversight in identifying model drift (especially in fast-paced sectors like energy), and the use of disparate tools in the model development process. This paper addresses these issues when developing AI solutions for NZEB projects by introducing ExplainerX, an integrated and explainable framework that streamlines all stages of building transparent and high-performance prediction models. ExplainerX ensures transparency in both data and results, accounts for data changes in dynamic settings, and promotes effective energy management in buildings. It leverages different components of the CRISP-DM methodology, providing explanations for each pipeline and tracing every decision made by data scientists and decision-makers. The framework is illustrated through a case study utilizing real datasets from the EU Improvement Project. ExplainerX source code is available on our GitHub repository.

As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, w.r.t. their context requirements (e.g., invocation time, location, social profiles, connectivity). However, current CASR lacks a rich context modelling and does not allow for multi-relational interactions between users and services in different contexts. We propose a context-sensitive service recommendation, by constructing a contextual service knowledge graph (C-SKG), which we translated into a low-dimensional vector space to facilitate its processing. Dilated Recurrent Neural Networks are applied to allow a context-aware C-SKG embedding, based on the principles of subgraph-aware proximity. A recommendation algorithm, finally, returns the top-rated services w.r.t. the target user’s context and the proximity degrees.

Given the large and growing volume of big data and frequent use of complex analytical queries, understanding energy efficiency of query processing has become a critical research issue, as highlighted by database systems papers in the last few years. Common software solutions mainly consider IO cost models to estimate energy consumption when executing queries. On the other hand, current hardware solutions benefit from advances in the development of green components and their associated tuning techniques, especially dynamic voltage and frequency scaling (DVFS), which can balance the performance and power consumption of multicore CPUs. Unfortunately, to the best of our knowledge, there is an absence of solutions mixing both (hardware and software). Heeding this gap, we propose a novel predictive model to measure and predict energy consumption of analytical queries when using multi-core processors and different frequency configurations. We first experimentally illustrate the surprising impact of CPU frequency and the number of processor cores on execution time, power and energy consumption. Second, we introduce an extended predictive model that enriches a well-known machine learning cost model with our new angle, the frequency scaling in multi-core environment. Specifically, by using Support Vector Regression and Random Forest Regression, we compute the energy coefficients of an accurate regression model for energy prediction. Experiments with benchmark data sets TPC-H and TPC-DS evaluate our proposed framework in terms of energy consumption reduction, showing promising results.

The concept of near-zero energy buildings (NZEBs) has materialized over the recent decades as a promising response to the ever-increasing energy consumption and CO2 emissions from buildings. An nZEB is a building that has a very high energy performance, by exploiting as much as possible from renewable energy sources. Both developed and developing countries are deploying efforts to produce NZEBs. The Improvement project is one example of initiatives from the European Union to provide the market with the appropriate tools for boosting the deployment of nZEBs. This deployment is conditioned by the value of the price of energy, which was stable from 2017 to 2020, but has become more volatile starting from the second half of 2021 onwards, with prices increasing rapidly. One of our roles in the EU IMPROVEMENT project is to develop accurate data-driven techniques for predicting the hourly market price of electricity in Portugal. By examining the literature, we have identified a large panoply of studies dealing with the forecast of energy prices. By deeply analyzing them, we realized that they fail to address unstable market conditions. It appears that each uses its own datasets and preparation processes to perform a prediction. To overcome those two major limitations, in this paper we propose ten (10) AI techniques for the forecast of electricity prices using the same dataset related to the Portuguese market between 2017 and 2022. Our techniques are belonging to statistical, Machine Learning (ML), and Deep Learning (DL) approaches, at several temporal granularities (hourly, daily, weekly, and monthly). The 10 techniques are implemented, tested, and evaluated with the MAE, RMSE, and CV metrics. Our results show that DL algorithms based on time series perform better even when changes in electricity prices become highly unpredictable, with large variations occurring. The XGBoost regression algorithm performs well when the dataset is smaller, with fairly stable price values.

Two decades ago, the most popular data mining technique were association rules (ARs). Nowadays deep neural networks (DNNs) are the most popular mechanism for building predictive models. On the other hand, medical data sets, despite being generally small in size (low volume), they are challenging for predictive models due to diverse attribute content (high variety) and variables with low redundancy (high variability). In this work we compare these two analytic techniques to identify effective models to predict heart disease, a multi-target prediction problem. Both techniques require expertise, manual tuning, and iterative experimentation to determine optimal parameters. Our goal is to build a DNN model that is at least as good as the best ARs. There exist two Big Data challenges: risks factors combined with imaging attributes produce a large number of hidden patterns and the number of association rules reaches millions, without using search constraints at low support (frequency) values. Preliminary experiments on a real data set show discovered rules have high predictive accuracy and they provide a highly accurate, but highly specific, profile of sick patients. Despite careful data pre-processing and hyper-parameter tuning DNNs are slightly more accurate than association rules, but more generalizable. Therefore, both techniques can complement each other.

We present GALGO, a system for large scale graph analytics. GALGO provides complex graph analytics in a parallel cluster, exploiting a parallel database system as a computation engine. In this demonstration we show that fundamental graph algorithms including all pairs shortest path, single source shortest path, PageRank, triangle counting, connected components and reachability can be solved completely with queries, dynamically generated by our system. Our system presents performance that is very competitive to state-of-the-art graph systems. Furthermore, our out-of-core graph computation can process graphs larger than available main memory, without compromising performance.

Over two decades, context awareness has been incorporated into recommender systems in order to provide, not only the top-rated items to consumers but also the ones that are suitable to the user context. As a class of context-aware systems, context-aware service recommendation (CASR) aims to bind high-quality services to users, while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and context information, users reviews and feedbacks). In addition, they lack a rich representation of contextual information, as they adopt a simple matrix view. Moreover, current CASR approaches adopt the traditional user-service relation and they do not allow for multi-relational interactions between users and services in different contexts. To offer a scalable and context-sensitive service recommendation with great analysis and learning capabilities, we provide a rich and multi-relational representation of the CASR knowledge, based on the concept of knowledge graph. The constructedcontext-aware service knowledge graph(C-SKG) is, then, transformed into a low-dimensional vector space to facilitate its processing. For this purpose, we adopt Dilated Recurrent Neural Networks to propose a context-aware knowledge graph embedding, based on the principles of first-order and subgraph-aware proximity. Finally, a recommendation algorithm is defined to deliver the top-rated services according to the target user's context. Experiments have proved the accuracy and scalability of our solution, compared to state-of-the-art CASR approaches.

Today’s common consensus is that the world’s most valuable resource is no longer oil but data. But, like oil, data is a source of pollution, mainly caused by the processing of extensive amounts of data. Thus, providers of data storage and processing solutions are at the heart of the debate on green computing. These solutions shall satisfy at the same time two conflictual non-functional requirements (NFRs): (i) the performance of analytical queries and (ii) the reduction of the energy consumption. These NFRs are strongly connected to query processors. Contrary to the first NFR, which has been widely studied by academia and industry, the second one does not get the same attention. The current works dealing with query processors’ energy efficiency (EE) are mainly focused on logical optimizations of database operations. However, nobody can deny that the satisfaction of the first NFR passes necessarily through physical optimizations such as indexes. Based on this discussion, we highly recommend the usage of green physical optimizations by existing and ongoing query processors. To promote and defend our vision of a green World, we propose in this paper to study the problem of selecting Bitmap Join Indexes that balance our NFRs. Because of its hardness, we first introduce a pruning strategy that eliminates non-relevant indexable attributes. Second, an approach for selecting indexes includes a hypergraph structure to manage the large search space of our problem and a Skyline operator to find the compromise between these NFRs. Third, we conduct intensive experiments to assess the impact of our proposal on our studied NFRs.

Nowadays large-scale data-centric systems have become an essential element for companies to store, manipulate and derive value from large volumes of data. Capturing this value depends on the ability of these systems in managing large-scale workloads including complex analytical queries. One of the main characteristics of these queries is that they share computations in terms of selections and joins. Materialized views (MV) have shown their force in speeding up queries by exploiting these redundant computations. MV selection problem (VSP) is one of the most studied problems in the database field. A large majority of the existing solutions follow workload-driven approaches since they facilitate the identification of shared computations. Interesting algorithms have been proposed and implemented in commercial DBMSs. But they fail in managing large-scale workloads. In this paper, we presented a comprehensive framework to select the most beneficial materialized views based on the detection of the common subexpressions shared between queries. This framework gives the right place of the problem of selection of common subexpressions representing the causes of the redundancy. The utility of final MV depends strongly on the selected subexpressions. Once selected, a heuristic is given to select the most beneficial materialized views by considering different query ordering. Finally, experiments have been conducted to evaluate the effectiveness and efficiency of our proposal by considering large workloads.

ER diagrams have a proven track record to rep-resent data structure and relationships, in many CS problems, beyond relational databases. The ER diagram strengths are abstraction, generality, flexibility, and intuitive visual representation, with few weaknesses; hence its popularity. The main con is the old box-diamond-ellipse-line notation, which has been subsumed by the more modern and simpler UML box-line notation. Given the broad, varied, and dynamic nature of big data ER diagrams are mostly ignored, except when the data sources are databases. It is common wisdom raw big data needs significant pre-processing before computing any analytics, resulting in a long chain of data transformations computed in SQL, Python, or R languages, for instance. On the other hand, flow diagrams remain the main mechanism to visualize major components of a software system or main processing steps of an algorithm, showing rectangles (verbs) connected by arrows (processing order, dependence). In this work, we propose to combine both diagrams into one. We propose a hybrid diagram, which we call ER-Flow, based on modern UML notation, to assist analysts in data pre-processing and exploration. Aiming to introduce a minimal change to the ER diagram, we extend relationships lines with an arrow, indicating processing flow and we annotate entities coming from pre-processing with numbers and transformation labels. We illustrate how our novel ER-Flow diagram can help the user navigate big data at the metadata level, providing an integrated view of data and source code, with many practical benefits.

Workload-driven approaches for partitioning and tuning traditional Parallel Database systems are well studied in the literature. Unfortunately, in the context of new generation "Big Data" warehouses, these approaches are not correctly adapted to Business Intelligence 2.0, where the analyst is at the heart of decision support systems. This "disconnect" situation strongly impacts both data partitioning and fragment allocation processes, which are essential to achieve good query performance. To overcome this problem, recent studies proposed online data partitioning and fragment allocation using AI techniques to improve query performance with adaptive behavior. Nevertheless, they have important limitations: they add significant overhead and they tend to focus on the current workload, ignoring query logs. With such motivation in mind, we first formulate the problem of optimizing database partitioning subject to feasibility constraints, based on a query workload. We then introduce a proactive partitioning approach combining offline and online processing phases, inspired by closed-loop control (used in engineering disciplines) and genetic algorithms (from AI). We present an experimental validation on a big data cluster that shows promising results on typical OLAP workloads.

Nowadays, energy reduction has become a critical and urgent issue for the database community. A lot of initiatives have been launched on energy-efficiency for intensive-workload computation covering individual hardware components, system software, to applications. This computation is mainly ensured by query optimizers. Their current versions minimize inputs-outputs operations and try to exploit RAM as much as possible, by ignoring energy. A couple of studies proposed the integration of energy into query optimizers that can be classified into hardware and software solutions. Several researchers have the idea that the operating systems and firmware manage energy and put software solutions in the second plan. This does not distinguish between tasks of operating systems and DBMSs. In this paper, we claim that building from scratch a green query processors and revisiting existing ones pass through 4-steps procedure: (1) establishment of a deep audit that allows understanding the query processor functioning, (2) identification of relevant energy-sensitive parameters belonging to hardware and software components, (3) elaboration of mathematical cost models estimating consumed energy when executing a query on a target DBMS and (4) setting of values of the energy-sensitive parameters using a nonlinear regression technique. To show the effectiveness of this procedure, we apply it on two open-source DBMSs with different functioning policies: PostgreSQL and MonetDB and compared them using the dataset and the workload of the TPC-H benchmark.

Triangles are an important pattern in large-scale graph analysis for their practical use in many real-life applications. However, with the expansion of networks, maintaining a balanced computational load is challenging especially for problems like triangle computations because of skewed vertices. On the other hand, there is a huge amount of data in database management systems (DBMSs) that can be modeled and analyzed as graphs. With these motivations in mind, we developed PandaSQL, a novel approach using SQL queries to enumerate all the triangles in a given graph based on Randomized Triangle Enumeration Algorithm. Our approach is elegant, abstract, and short compared to traditional languages like C++ or Python. Moreover, our partitioning queries ensures perfect load balancing. Thus, the triangle enumeration is independent, local, and parallel.

In critical situations, making quick and precise decisions requires a rapid execution of a large amount of concurrent navigational and exploratory queries over collected data stored in repositories such as data warehouses. To satisfy the decision-maker's requirement, a deep understanding of the properties of these queries is necessary. In addition to their large-scale, they are ad-hoc, dynamic and highly interacted. By a quick analysis of these properties, we figure out that the first three are factual whereas the last one is behavioral. The literature has widely reported that the interaction of analytical queries has a crucial impact on selecting optimization structures (e.g., materialized views) in data storage systems. By keeping these four properties in mind, it becomes a necessity to find scalable and efficient data structures to simultaneously model them for better optimization of large-scale queries. In this paper, we first show the crucial role of the interaction phenomenon in optimizing concurrent data and mining queries by identifying its limited capacity in considering all factual properties. Secondly, we propose a dynamic hypergraph as a data structure to manage the four above properties and we show its great contribution in selecting materialized views. Finally, intensive experiments are conducted to evaluate the efficiency of our proposal and its connectivity with a commercial DBMS.

Energy consumption is increasingly more important in large-scale query processing. This problem requires revisiting traditional query processing in actual DBMSs to identify the potential of energy saving, and to study the trade-offs between energy consumption and performance. In this paper, we propose EnerQuery, a tool built on top of a traditional DBMS to capitalize the efforts invested in building energy-aware query optimizers, which have the lion's share in energy consumption. Energy consumption is estimated on all query plan steps and integrated into a mathematical linear cost model used to select the best query plans. To increase end users' energy awareness, EnerQuery features a diagnostic GUI to visualize energy consumption per step and its savings when tuning key parameters during query execution.

In the era of globalization, it is strongly recommended for a company when developing a new project to identify companies (at local and international levels) that have already developed similar projects to reuse their experience and reproduce their findings. It should be noticed that the reuse is an eternal grail to increase projects productivity. It may concern all aspects of the life cycle of project development that ranges from user requirements to codes. In this study, we focus on user requirements. In such a context, each company (site) has already expressed its own requirements using different modeling formalisms, conceptualizations and vocabularies and scheduling of tasks. This situation makes the reuse and exploitation of the requirements harder, since the heterogeneity is everywhere. To reduce this heterogeneity, the most important studies assume the existence of pivot models and shared ontologies that allow reducing heterogeneities. Unfortunately, these hypotheses are out of phase with the reality, in which the sites are autonomous and usually multilingual. In this paper, we propose to relax these hypotheses. Firstly, we propose a framework for requirements integration using an ontology-based matching system that identifies and unifies different heterogeneous items. Secondly, a reasoning mechanism is proposed to deduce relationships between integrated requirements. Finally, our proposed framework is evaluated w.r.t its feasibility, results quality and effectiveness. A tool, called MURGROOM implementing the framework is also proposed.

In the Big Data Era, the management of energy consumption by servers and data centers has become a challenging issue for companies, institutions, and countries. In data-centric applications, DBMS are one of the major energy consumers when executing complex queries involving very large databases. Some research has been devoted to this issue, covering both the hardware and software dimensions. Regarding software, several proposals have been outlined, focusing either on analytical cost models to predict energy when executing queries or techniques to save energy. To this date, no research has taken account of energy at the physical design level, a crucial phase in database design. In this paper, we propose a methodology, called Eco-DMW, that integrates the energy dimension into the physical design. To show this integration, we study the case of materialized views, a redundant optimization structure. We first show the place that energy takes throughout this stage of design. A multi-objective formalization of the problem of materialized view selection is given. A genetic algorithm is developed to solve the problem. Intensive experiments are conducted using a mathematical cost model and a real measurement tool dedicated to computing energy. Results show the interest of this proposal to save energy and optimize queries in the presence of the selected materialized views.

The spectacular development of business intelligence applications (BIA), built around the data warehousing technology, increases the demand on query performance of DBMS hosting with its extremely high amount of data. In such a context a high interaction among queries exists since they share a large number of intermediate results. This is due to the fact that BIA use relational schemes such as a star schema in which each join passes through the fact table. The decision to cache these intermediate results in the traditional buffer becomes a critical issue since it depends on the size of the buffer and the number of intermediate results candidate for caching. As flash memory is more and more adopted in mass storage systems, we rely on it to buffer some intermediate results. In this paper, we first propose to couple the RAM and Solid State Drive, to respond to the problem combining buffer management and query scheduling sub problems. Secondly, a cost model for evaluating the quality of buffering data and scheduling queries is given. Based on this cost model, an algorithm is given to solve our joint problem. Simulations show that our proposal enhances the performance of SQL queries up to 86%.

The ACM DOLAP workshop presents research on data warehousing and On-Line Analytical Processing (OLAP). The DOLAP 2013 program has three interesting sessions on Design and Exploitation of Social Data Warehouses, ETL and modeling and new trends, as well as a keynote talk on OLAP query processing and a panel on OLAP and DataWarehousing Technology in Big Data era.

In this paper, we highlight open problems and actual research trends in the field of Data Warehousing and OLAP over Big Data, an emerging term in Data Warehousing and OLAP research. We also derive several novel research directions arising in this field, and put emphasis on possible contributions to be achieved by future research efforts.

Cost models have been following the life cycle of databases. In the first generation, they have been used by query optimizers, where the cost-based optimization paradigm has been developed and supported by most of important optimizers. The spectacular development of complex decision queries amplifies the interest of the physical design phase (PhD), where cost models are used to select the relevant optimization techniques such as indexes, materialized views, etc. Most of these cost models are usually developed for one storage device (usually disk) with a well identified storage model and ignore the interaction between the different components of databases: interaction between optimization techniques, interaction between queries, interaction between devices, etc. In this paper, we propose a generic cost model for the physical design that can be instantiated for each need. We contribute an ontology describing storage devices. Furthermore, we provide an instantiation of our meta model for two interdependent problems: query scheduling and buffer management. The evaluation results show the applicability of our model as well as its effectiveness.

In order to successfully benefit from global markets, companies must expand their activities over the world. This expansion is usually performed by the development of software involving all heterogeneous and autonomous partners. The user requirement collection and analysis is one of the most important phase of the life cycle of software development. In the context of globalization, this phase becomes a challenging issue. This is because designers of each branch of a given global company may use different vocabulary and formalism to express their requirements. Solutions exist in the literature to unify either vocabularies or formalisms, but not both. In this paper, we propose a semantic and scalable approach that unifies the vocabularies and formalisms by the means of ontologies. The capabilities of reasoning offered by ontologies is exploited to identify the inconsistencies in an efficient way. Our approach is validated by a case tool using three formalisms: the UML use case, the goal oriented and the treatment conceptual model of Merise method.

In this paper, a comprehensive methodology for designing and querying Parallel Rational Data Warehouses (PRDW) over database clusters, called Fragmentation & Allocation (F&A) is proposed. F&A assumes that cluster nodes are heterogeneous in processing power and storage capacity, contrary to traditional design approaches that assume that cluster nodes are instead homogeneous, and fragmentation and allocation phases are performed in a simultaneous manner. In classical approaches, two different cost models are used to perform fragmentation and allocation, separately, whereas F&A makes use of one cost model that considers fragmentation and allocation parameters simultaneously. Therefore, according to the F&A methodology proposed, the allocation phase/decision is done at fragmentation. At the fragmentation phase, F&A uses two well-known algorithms, namely Hill Climbing (HC) and Genetic Algorithm (GA), which the authors adapt to the main PRDW design problem over heterogeneous database clusters, as these algorithms are capable of taking into account the heterogeneous characteristics of the reference application scenario. At the allocation phase, F&A introduces an innovative matrix-based formalism capable of capturing the interactions among fragments, input queries, and cluster node characteristics, driving the data allocation task accordingly, and a related affinity-based algorithm, called F&A-ALLOC. Finally, their proposal is experimentally assessed and validated against the widely-known data warehouse benchmark APB-1 release II.

Ontologies become more popular in various domains. In database, they contribute largely in designing operational databases, that we call ontology-based databases (OBDB). An OBDB stores ontology and their instances in the same repository. Several DBMS propose solutions to manipulate and query this type of databases. As consequence, these operational OBDB become a candidate to feed data warehouses (DW). In this paper, we propose a new conceptual design methodology of DW from data residing in various OBDB that takes into account sources and decision maker requirements. As the result, a semantic DW model is generated. The presence of ontology within a DW facilitates its querying in semantic level and its future integration with other DW built using our methodology. Finally, we present a case tool, called S2RWC, supporting our proposal.

Most of business intelligence applications use data warehousing solutions. The star schema or its variants modelling these applications are usually composed of hundreds of dimension tables and multiple huge fact tables. Referential horizontal partitioning is one of physical design techniques adapted to optimize queries posed over these schemes. In referential partitioning, a fact table can inherit the fragmentation characteristics from dimension table(s). Most of the existing works done on referential partitioning start from a bag containing selection predicates defined on dimension tables, partition each one based on its predicates and finally propagate their fragmentation schemes to the fact table. This procedure gives all dimension tables the same probability to partition the fact table which is not always true. In order to ensure a high performance of the most costly queries, the identification of relevant dimension table(s) to referential partition a fact table is a crucial issue that should be addressed. In this paper, we first study the complexity of the problem of selecting dimension table(s) used to partition a fact table. Secondly, we present strategies to perform their selection. Finally, to validate of our proposal, we conduct intensive experimental studies using a mathematical cost model and the obtained results are verified on Oracle11G DBMS.

OLAP users heavily rely on visualization of query answers for their interactive analysis of massive amounts of data. Very often, these answers cannot be visualized entirely and the user has to navigate through them to find relevant facts.In this paper, we propose a framework for personalizing OLAP queries. In this framework, the user is asked to give his (her) preferences and a visualization constraint, that can be for instance the limitations imposed by the device used to display the answer to a query. Given this, for each query, our method computes the part of the answer that respects both the user preferences and the visualization constraint. In addition, a personalized structure for the visualization is proposed.

Article On efficient storage space distribution among materialized views and indices in data warehousing environments Share on Authors: Ladjel Bellatreche University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong KongView Profile , Kamalakar Karlapalem University of Science & Technology, Clear Water Bay, Kowloon, Hong Kong University of Science & Technology, Clear Water Bay, Kowloon, Hong KongView Profile , Michel Schneider Université Blaise Pascal, 63177 Aubière Cédex, France Université Blaise Pascal, 63177 Aubière Cédex, FranceView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 397–404https://doi.org/10.1145/354756.354846Online:06 November 2000Publication History 8citation642DownloadsMetricsTotal Citations8Total Downloads642Last 12 Months5Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Efficient query processing is a critical requirement for data warehousing systems as decision support applications often require minimum response times to answer complex, ad-hoc queries having aggregations, multi-ways joins over vast repositories of data. This can be achieved by fragmenting warehouse data. The data fragmentation concept in the context of distributed databases aims to reduce query execution time and facilitates the parallel execution of queries. In this paper, we propose a methodology for applying the fragmentation technique in a data warehouse star schema to reduce the total query execution cost. We present an algorithm for fragmenting the tables of a star schema. During the fragmentation process, we observe that the choice of the dimension tables used in fragmenting the fact table plays an important role on overall performance. Therefore, we develop a greedy algorithm in selecting "best" dimension tables. We propose an analytical cost model for executing a set of OLAP queries on a fragmented star schema. Finally, we conduct some experiments to evaluate the utility of the fragmentation for efficiently executing OLAP queries.

In a distributed deductive database system, queries which invoke rules executing at different sites and access different data need to be executed rather efficiently. Therefore, the rules invoked and relations accessed by the queries need to be allocated to sites so as to reduce the data transfer cost in processing a given set of queries. The rules and data allocation problem needs to take into consideration complex interdependencies among queries, rules and data. In this paper, we develop a comprehensive cost model for total data transfer incurred in processing a given set of queries by incorporating the dependencies among queries, rules and data. Furthermore, we develop an iterative approach to generate near-optimal solution for the combined rules and data allocation problem by using our cost model. In this approach, we start with an initial data allocation which is used for rule allocation, which in turn is used for data allocation, and so on. We stop this iterative rules and data allocation procedure when there is no further reduction in total data transfer cost incurred in processing the given set of queries. We also present the results of experiments conducted to evaluate the effectiveness of our approach by comparing the results with the exhaustive enumeration solution (which guarantees the optimal solution).