2014 Proceeding- General Chair:
- Il-Yeol Song,
- Program Chairs:
- Alkis Simitsis,
- Alfredo Cuzzocrea
It is our great pleasure to welcome you to the 17th ACM International Workshop on Data Warehousing and OLAP (DOLAP 2014). The DOLAP workshop continues its tradition of being a premier forum where both researchers and practitioners in Data Warehousing and On-Line Analytical Processing (OLAP) share their findings in theoretical foundations, methodologies, physical designs, new trends, and practical experiences. The mission of the DOLAP workshop is to identify and explore new directions for future research and development, as well as emerging application domains in the areas of data warehousing and OLAP. Traditional solutions and architecture designs in Data Warehousing and OLAP are evolving to cope with emerging application domains and data types, such as MPP, big data infrastructures, and analytics. In recent years, research in these areas has addressed emerging topics and has provided solutions toward building effective decision-support applications, deploying them on modern hardware, and aiming at improving optimization objectives as system performance.
The call for papers attracted 24 submissions (22 regular papers and 2 short papers) from 17 different countries. The program committee reviewed and accepted 8 full papers and 4 short papers, resulting to an acceptance rate of 36% for full papers and 50% overall. The accepted papers span a wide variety of topics, including query processing and physical design, security issues for cloud data warehouses, handling of 'exotic' data like genomic and GPS data, location intelligence, optimization and data generation for data flows, and modeling issues for movement data, analytical metadata, and OLAP sessions. The program also includes an invited keynote talk by Prof. Minos Garofalakis from the Technical University of Crete, on querying big, dynamic, distributed data, and a panel on how DW and OLAP technologies can be used in big graph analytics.
Proceeding Downloads
Querying Big, Dynamic, Distributed Data
Effective Big Data analytics pose several difficult challenges for modern data management architectures. One key such challenge arises from the naturally streaming nature of big data, which mandates efficient algorithms for querying and analyzing ...
GOLAM: A Framework for Analyzing Genomic Data
The emerging medical models aim at leveraging on high-throughput genome sequencing technologies to better target drugs to patients' personal profiles so as to increase their effectiveness. However, the huge amount of data made available by these ...
An Advanced Data Warehouse for Integrating Large Sets of GPS Data
GPS data recorded from driving vehicles is available from many sources and is a very good data foundation for answering traffic related queries. However, most approaches so far have not considered combining GPS data from many sources into a single data ...
Bijoux: Data Generator for Evaluating ETL Process Quality
Obtaining the right set of data for evaluating the fulfillment of different quality standards in the extract-transform-load (ETL) process design is rather challenging. First, the real data might be out of reach due to different privacy constraints, ...
From Business Intelligence to Location Intelligence with the Lily Library
Location intelligence is a set of tools and techniques to integrate spatial features into BI platforms, aimed at better monitoring and interpreting business events related to the territory. In this demonstration we present Lily, a geo-enhanced library ...
A Holistic Approach to OLAP Sessions Composition: The Falseto Experience
OLAP is the main paradigm for flexible and effective exploration of multidimensional cubes in data warehouses. During an OLAP session the user analyzes the results of a query and determines a new query that will give her a better understanding of ...
A Semantic Model for Movement Data Warehouses
Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajectories, episodes like stops ...
SM4AM: A Semantic Metamodel for Analytical Metadata
Next generation BI systems emerge as platforms where traditional BI tools meet semi-structured and unstructured data coming from the Web. In these settings, the user-centric orientation represents a key characteristic for the acceptance and wide usage ...
A Framework for User-Centered Declarative ETL
As business requirements evolve with increasing information density and velocity, there is a growing need for efficiency and automation of Extract-Transform-Load (ETL) processes. Current approaches for the modeling and optimization of ETL processes ...
Recursive Query Evaluation in a Column DBMS to Analyze Large Graphs
Graphs represent a major challenge on big data analytics, for which there are many systems and prototypes, most of them not based on relational database management systems (DBMSs). Graph problems require substantially different algorithms compared to ...
fVSS: A New Secure and Cost-Efficient Scheme for Cloud Data Warehouses
Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this paper, we ...
What can Emerging Hardware do for your DBMS Buffer?
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 ...
Optimization of Data-intensive Flows: Is it Needed? Is it Solved?
Modern data analysis is increasingly employing data-intensive flows for processing very large volumes of data. As the data flows become more and more complex and operate in a highly dynamic environment, we argue that we need to resort to automated cost-...
Big Graph Analytics: The State of the Art and Future Research Agenda
Analytics over big graphs is becoming a first-class challenge in database research, with fast-growing interest from both the academia and the industrial community. This problem arises in several application scenarios, ranging from social networks to ...
Index Terms
- Proceedings of the 17th International Workshop on Data Warehousing and OLAP
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