Recently, the needs of data required for data analysis are becoming more diversified, and research on data extraction and analysis methods has been continuously made in order to effectively respond to various needs. Process mining is a solution that analyzes various system logs built by companies or healthcare institutions so that they can be used for process improvement. From the process model extracted from the system logs, it is possible not only to grasp the exact flow of the current business process, but also to acquire additional information such as repetitive execution of activities in the process where the bottleneck occurs in the business process flow. The manufacturing industry has made great efforts to improve the process management, and as many companies are paying attention to big data these days, various data-related technologies are emerging in the healthcare industry as well to properly provide patients with the care needed. Process mining tools allow users to pull data by programming in a process mining query language using the APIs provided with the process mining tool, or by manually creating reusable analytical documents using user friendly tool. However, these tasks require the users to be familiar with the query language APIs and understand the data model and its relationships with respect to creating analytical documents. This paper proposes a methodology that allows users to easily extract desired data through natural language interface, which relieves nonprofessional users of the burden of programming in a process mining query language. The process mining query engine with natural language interface presented in this paper consists of four major components. Among them, the natural language processing pipeline that not only extracts intermediate representation of entities used when constructing a process mining query language report from natural language queries, but also effectively extracts a query hint from the context of natural language query. The query hint is used to select a process-specific function from the library that fits the context of the user query while transforming a natural language query into a process mining query report. The method proposed in this study has the advantage of being able to roughly grasp the process state for the user just by entering a query in natural language. The proposed system provides users with four query process options. That is, the user 1) retrieves intermediate representation of entities and query hints from the NLP pipeline, 2) retrieves the process mining query language from the query language generator, 3) submits the query language to the process mining engine and execute the query, 4) retrieves description of intermediate representation of entity and query hints in natural language to confirm that the query is processed correctly. The contents proposed in this paper were constructed and executed, and the query reports in process mining query language programmatically generated by the proposed query engine were also executed in a process mining engine and the query results were verified.

Database management systems offer an efficient way of managing huge amount of data such as financial and healthcare data and he data retrieval from databases requires knowledge of Structured Query Language (SQL). In this paper, an Automatic SQL Generation System is proposed to help users who are inexperienced in querying database with SQL. The proposed SQL generation system reads formatted data items in the query report from the user and converts the data items into SQL statements programmatically with the help of a data model that is pulled from a database. The SQL generation system can handle simple queries composed of a query block with a SELECT statement as well as complex queries composed of multiple query blocks containing multiple SELECT statements. The proposed system is integrated with an NLIDB (Natural Language Interface for Database) system to translate data items (or tokens) extracted from queries in natural languages into SQL query language, and the system is also integrated and adapted with various types of databases and use cases that include financial and healthcare use cases. The experiment results show that the proposed system correctly handles user queries in natural language just like any other neural model based system and more importantly, the proposed SQL generation engine generates SQL queries without syntactic problems with various databases for all queries.

Number of use cases in healthcare are well suited as Big Data applications. In healthcare, large volumes of data are coming in and stored as unstructured big data or as structured data in relational database. In any case, Big Data is coming to embrace SQL as a common tool for querying. Developing a question and answering tool for the users that are lack of specialized skillsets and use natural languages for complex queries is a challenge that need to identify significant details, draw inferences and evaluate hypothesis as how domain experts do those. Although NLIDB systems are developed to translate a natural language queries into a database language for non-technical end users, most of the questions addressed by the systems are factoid questions and answering complex queries remains as an open research problem. The proposed auxiliary system is machine learning based and extends existing NLIDB system to help it answer the complex queries. The auxiliary system mimics the way human experts reach the answers to the complex queries. Instead of building a set of simple conditional statements as rules and invoke them as a sequence of chained actions, the proposed system decomposes complex queries into multiple simple factoid sub-queries with the goal of generating answers to each sub-query with the existing NLIDB system from the data explicitly stored in the database. The underlying NLIDB system takes sub-queries as input queries in parallel and produces query results from the data stored in the relational database. The answers to the sub-queries and the desired output labels are used to train the model and the multiclass classifier produced from the training is used to predict and answer valid input queries.

Healthcare applications typically require big data management as well as intensive computation. This is especially true with recently developed next generation sequencing technology which increases interests in processing the huge amount of information in a timely fashion. In this paper, we focus on testing whether the healthcare applications can scale well on commercial big data platforms that implement MapReduce framework. We selected short read sequence alignment and assembly workloads in genome analysis workloads, and chose Bowtie, Blast and Contrail-bio which are publically available applications designed to run on the Hadoop MapReduce framework. To speed-up the processes we compressed the intermediate data using various compression schemes the compression schemes are compared. The test results are very promising and indicate that the wide range of genomic analysis workflows can be optimized on MapReduce frameworks with great computational efficiency and scalability.

Cloud service providers are offering the popular Hadoop analytics platform following an "as-a-service" model, i.e. clusters of machines in their cloud infrastructures pre-configured with Hadoop software. Such offerings lower the cost and complexity of deploying a comparable system on-premises, however security considerations and in particular data confidentiality hamper wider adoption of such services by enterprises that handle data of sensitive nature. In this paper, we describe our efforts in providing security for data-at-rest (i.e. data that is stored) when Hadoop is offered as a cloud service. We analyze the requirements and architecture for such service and further describe a new distributed file system that we developed for Hadoop called SDFS, towards supporting this premise. We analyze parameter tuning for SDFS and through experiments on a real test-bed we evaluate its performance. We further present simulation results that explore the parameter space and can guide tuning.