Narrative-based writing for coherent technical documents

Narrative-based writing is a technique that was developed to address the lack of support for document coherence. The technique depends on the production of a story-like executive summary of the document called a DN (Document Narrative). This is then analysed using a discourse theory called Rhetorical Structure Theory (RST) which helps further to correct any lapses in coherence in the DN before proceeding to use it to write the document. Previous papers have described the technique briefly, alongside discussions of the ongoing software development to incorporate narrative support in writing tools. It has now become apparent that the technique itself needs to be explained in greater detail. This is the purpose of this paper. Here, narrative-based writing and the reasoning behind it is described. This is followed by a description of a user experiment conducted in May 2006 to evaluate narrative-based writing and discover areas in which it could be improved. The positive feedback from the volunteers has motivated us to continue to refine and simplify the technique

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DATA_SPHERE

This paper presents a comprehensive overview of Database Management Systems (DBMS) and their significance in modern information management. DBMS technology plays a crucial role in the storage, organisation, retrieval, and manipulation of vast amounts of data in various domains, ranging from business operations to scientific research. This abstract highlights the key aspects covered in the paper, including the evolution of DBMS, its architectural components, and the challenges and advancements in the field. The paper begins by discussing the historical development of DBMS, tracing its origins from file-based systems to the emergence of relational databases and the subsequent rise of object-oriented and NoSQL databases. We explore the motivations behind these advancements and their impact on data management. Next, we delve into the fundamental architectural components of a DBMS. We examine the storage layer, which encompasses data structures and access methods, and discuss different indexing techniques for efficient data retrieval. The query processing and optimization module are explored, focusing on query execution plans and cost-based optimization strategies. Additionally, we analyse the transaction management component, highlighting concepts such as ACID properties, concurrency control, and recovery mechanisms. The abstract also highlights the challenges faced by modern DBMS. With the proliferation of big data and the advent of cloud computing, scalability, availability, and performance have become critical concerns. We examine techniques such as parallel and distributed databases, replication, and sharding to address these challenges. Furthermore, we discuss the integration of DBMS with emerging technologies like machine learning and blockchain to leverage their capabilities in data analytics and secure data transactions. Lastly, the abstract touches upon recent advancements in DBMS, including the rise of graph databases for managing interconnected data, the adoption of in-memory databases for high-performance applications, and the exploration of new database models to handle unstructured and semi-structured data. In conclusion, this paper provides a comprehensive overview of DBMS, covering its historical evolution, architectural components, challenges, and recent advancements. By understanding the principles and advancements in DBMS, researchers and practitioners can effectively harness the power of data management systems to tackle the complexities of modern data-driven applications