Apache Mahout is a library for scalable machine learning (ML) on distributed dataflow systems, offering various implementations of classification, clustering, dimensionality reduction and recommendation algorithms. Mahout was a pioneer in large-scale machine learning in 2008, when it started and targeted MapReduce, which was the predominant abstraction for scalable computing in industry at that time. Mahout has been widely used by leading web companies and is part of several commercial cloud offerings. In recent years, Mahout migrated to a general framework enabling a mix of dataflow programming and linear algebraic computations on backends such as Apache Spark and Apache Flink. This design allows users to execute data preprocessing and model training in a single, unified dataflow system, instead of requiring a complex integration of several specialized systems. Mahout is maintained as a community-driven open source project at the Apache Software Foundation, and is available under https://mahout.apache.org.

In this study we present a text categorization approach for stylometric analysis of Turkish documents. To this end we extend traditional features and take advantage of linguistic processing. We experiment with a combination of bag of stems and additional features such as function words, part of speech tags and morpho-syntactic tags in datasets having varying number of authors. Based on the characteristics of Turkish (agglutinative language) we expected morpho-syntactic tags to perform better. However, neither part of speech tags nor morpho syntactic tags has showed a significant gain in our settings. Our findings suggest that the main performance is dominated with bag of stem features and the best performance is achieved with combination of bag of stems and function words.

The amount of data that businesses collect and analyze has been rapidly increasing, which has triggered an increase in big data teams. With the growth of both the number and size of big data teams, specialized roles are starting to be defined. One such role is the data engineer, who focuses on ensuring that the data is easily available for advanced analytics. Via a case study, this paper explores the role of the data engineer and the key characteristics that enable someone to be a good data engineer. The paper also explores if good software engineers could become good data engineers. Our findings show that the knowledge and skills required to be a data engineer are significantly different from those required to be a software engineer. Hence, not surprisingly, we found that that not all software engineers could become good data engineers.

We introduce a method to help people to refine their search queries based on other people's similar queries. We compared two different methods of collaborative filtering on search logs of a popular e-commerce site to do query expansion. In item-based method, by identifying similarities between different items, and in user-based method, by identifying similarities between different users, we compute recommendations for users. The experimental results show that item-based query expansion method provides better performance than the user-based method. While the user-based method improved the system performance 20%, the item-based method provided success of 73%. Also, item-based method recommended better expansion terms than the user-based method, which is important in helping Web users to easily access information needs by formulating qualified queries. This document is a comparison of these two methods, and their effects on system performance.

In this poster, we will present the results of efforts we have undertaken to conduct evaluations of a QA system in a real world environment and to understand the nature of the dimensions on which users evaluate QA systems when given full reign to comment on whatever dimensions they deem important.

The poster reports on a project in which we are investigating methods for breaking the human metadata-generation bottleneck that plagues Digital Libraries. The research question is whether metadata elements and values can be automatically generated from the content of educational resources, and correctly assigned to mathematics and science educational materials. Natural Language Processing and Machine Learning techniques were implemented to automatically assign values of the GEMgenerate metadata element set tofor learning resources provided by the Gateway for Education (GEM), a service that offers web access to a wide range of educational materials. In a user study, education professionals evaluated the metadata assigned to learning resources by either automatic tagging or manual assignment. Results show minimal difference in the eyes of the evaluators between automatically generated metadata and manually assigned metadata.