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.

Analysis of legal and contract documents often requires both the discovery of document structure, as well as the accurate identification of important elements such as party (buyer, supplier), nature (obligation, right) and category (warranties, delivery, etc.). Hence, exploring novel features that lead to better element classification accuracy as well as better document structure discovery is particularly important. In this paper, we develop and present novel unsupervised learning techniques to analyze a large scale corpus of contract documents with the goal of learning and deriving new features to enhance classification accuracy over the elements of interest, and to extract relevant features leading to meaningful clusterings over contract structures. Particularly, we propose a novel t-threshold autoencoder neural network that flexibly controls the number of active neurons in response to sentences of different lengths at the network's input. Such an adaptive sparseness threshold enforces competition and specialization among encoding neurons and hence results in better features learning. We also present an extension of the convolutional neural network classifier that allows for the incorporation of these novel augmented features and show that higher classification accuracies over various classes of contract elements can be achieved. We further present a practical pipeline of deriving features from contract documents along with a clustering solution based on the K-means algorithm that leads to the separation among different types of sentences in the contract documents. We empirically demonstrate the performance of our developed techniques on a novel data corpus of Software Procurement contracts.

Automated Ontology Learning systems are nowadays practical and used in a variety of domains. By using these systems, subject matter experts (SMEs) and ontology designers can readily construct very large ontologies consisting of tens of thousands of concepts and their relations based on a corpus. However, ontologies of this size make it extremely challenging for such SMEs to understand and further tune these ontologies. Prior studies have proposed techniques for concept ranking based solely on the analysis of the structure of the ontology graphs. In this paper, we propose a novel approach, which further exploits a word-level summarization technique applied to the source documents used to generate the ontology. Using the document summarization technique, we devise features that measure concept importance based on source documents where concepts are extracted. We demonstrate the effectiveness of our approach by comparing with existing ranking methods and by devising a scalable evaluation process inspired from the document retrieval domain.

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.