A Domain-Specific Language for Do-It-Yourself Analytical Mashups

The increasing amount and variety of data available in the web leads to new possibilities in end-user focused data analysis. While the classic data base technologies for data integration and analysis (ETL and BI) are too complex for the needs of end users, newer technologies like web mashups are not optimal for data analysis. To make productive use of the data available on the web, end users need easy ways to find, join and visualize it. We propose a domain specific language (DSL) for querying a repository of heterogeneous web data. In contrast to query languages such as SQL, this DSL describes the visualization of the queried data in addition to the selection, filtering and aggregation of the data. The resulting data mashup can be made interactive by leaving parts of the query variable. We also describe an abstraction layer above this DSL that uses a recommendation-driven natural language interface to reduce the difficulty of creating queries in this DSL

The influence that WEKA workbench has in processing information

Information is very important nowadays; as a result transforming data into information hassignificantly increased the importance of using Data Mining. To give sense to data, data mininguses several techniques that developed within a field known machine learning. In this paper wewill be taking a look at WEKA workbench, which is a collection of machine learningalgorithms and data preprocessing tools. We will discuss how WEKA functions andwhat benefits does the use of this workbench give to us. Later we will reach in a concrete case ofstudying that implements WEKA workbench inside another application. We will illustrate indetail how the panels of the Explorer interface, the main interface of WEKA, use data miningalgorithms to present the desired result of the explored data

Semantic data ingestion for intelligent, value-driven big data analytics

In this position paper we describe a conceptual model for intelligent Big Data analytics based on both semantic and machine learning AI techniques (called AI ensembles). These processes are linked to business outcomes by explicitly modelling data value and using semantic technologies as the underlying mode for communication between the diverse processes and organisations creating AI ensembles. Furthermore, we show how data governance can direct and enhance these ensembles by providing recommendations and insights that to ensure the output generated produces the highest possible value for the organisation

Data warehouse applications in modern day business

Data warehousing provides organizations with strategic tools to achieve the competitive advantage that organazations are constantly seeking. The use of tools such as data mining, indexing and summaries enables management to retrieve information and perform thorough analysis, planning and forcasting to meet the changes in the market environment. in addition, The data warehouse is providing security measures that, if properly implemented and planned, are helping organizations ensure that their data quality and validity remain intact

Migrating Traditional Web Applications to CMS-based Web Applications

AbstractIn recent years, Content Management Systems (CMS) have proven to be the best platforms for maintaining the large amount of digital content managed by Web applications. Thus, many organizations have experienced the necessity to base its Web applications on these CMS platforms. To do this, they start a migration process which is complex and error prone. To support this process, we propose a method based on the principles of Architecture-Driven Modernization (ADM) which automates the migration of Web applications to CMS-based Web applications. This article focuses on the implementation of two artifacts of this method: 1) the DSL ASTM_PHP, a modeling language for defining a model from PHP code (ASTM_PHP model) and 2) the model-to-model transformation rules which generate automatically a KDM model from a ASTM_PHP model. To show the feasibility of this implementation, we use a case study based on a widget implemented in PHP which lists the online users of a Web application

Reimagining Retrieval Augmented Language Models for Answering Queries

We present a reality check on large language models and inspect the promise of retrieval augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP task

Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous