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Abstract-This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), On-Line Analytical Processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the Web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the "passive" semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

Abstract-Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization, and insertion into a data warehouse. In this paper, we delve into the logical optimization of ETL processes, modeling it as a state-space search problem. We consider each ETL workflow as a state and fabricate the state space through a set of correct state transitions. Moreover, we provide an exhaustive and two heuristic algorithms toward the minimization of the execution cost of an ETL workflow. The heuristic algorithm with greedy characteristics significantly outperforms the other two algorithms for a large set of experimental cases.

One of the main tasks in the early stages of a Data Warehouse project is the identification of the appropriate transformations and the specification of inter-schema mappings from the data sources to the Data Warehouse. In this paper, we propose an ontology-based approach to facilitate the conceptual design of the back stage of a Data Warehouse. A graph-based representation is used as a conceptual model for the datastores, so that both structured and semi-structured data are supported and handled in a uniform way. The proposed approach is based on the use of Semantic Web technologies to semantically annotate the data sources and the Data Warehouse, so that mappings between them can be inferred, thereby resolving the issue of heterogeneity. Specifically, a suitable application ontology is created and used to annotate the datastores. The language used for describing the ontology is OWL-DL. Based on the provided annotations, a DL reasoner is employed to infer semantic correspondences and conflicts among the datastores and propose a set of conceptual operations for transforming data from the source datastores to the Data Warehouse.

Next generation business intelligence involves data flows that span different execution engines, contain complex functionality like data/text analytics, machine learning operations, and need to be optimized against various objectives. Creating correct analytic data flows in such an environment is a challenging task and is both labor-intensive and timeconsuming. Optimizing these flows is currently an ad-hoc process where the result is largely dependent on the abilities and experience of the flow designer. Our previous work addressed analytic flow optimization for multiple objectives over a single execution engine. This paper focuses on optimizing flows for a single objective, namely performance, over multiple execution engines. We consider flows that span a DBMS, a Map-Reduce engine, and an orchestration engine (e.g., an ETL tool or scripting language). This configuration is emerging as a common paradigm used to combine analysis of unstructured data with analysis of structured data (e.g., NoSQL plus SQL). We present flow transformations that model data shipping, function shipping, and operation decomposition and we describe how flow graphs are generated for multiple engines. Performance results for various configurations demonstrate the benefit of optimization.