<p>Online communication has increased the need to rapidly interpret complex emotions due to the volatility of the data involved; machine learning tasks that process text, such as sentiment analysis, can help address this challenge by automatically classifying text as positive, negative, or neutral. However, while much research has focused on detecting offensive or toxic language online, there is also a need to explore and understand the ways in which people express positive emotions and support for one another in online communities. This is where sentiment dictionaries and other computational methods can be useful, by analyzing the language used to express support and identifying common patterns or themes.</p> <p><br></p> <p>This research was conducted by compiling data from social question and answering around machine learning on the site Stack Exchange. Then a classification model was constructed using binary logistic regression. The objective was to discover whether predictions of marked solutions are accurate by treating the comments as reviews. Measuring collaboration signals may help capture the nuances of language around support and assistance, which could have implications for how people understand and respond to expressions of help online. By exploring this topic further, researchers can gain a more complete understanding of the ways in which people communicate and connect online.</p>

本研究之主要目標在於建構一個能評估分散式關聯資料庫中之特色的需求導向績效評估方法。在過去的績效評估研究中，已經有許多人對於關聯式資料庫績效評估做了多方面的努力。但是，過去的關聯式資料庫資效評估方法如：Wisconsin、AS3AP、TPC系列的Benchmarks都有著一些限制及不足的地方。 過去的關聯式資料庫績效評估方法並無法完全的評估出分散式資料庫的特殊需求及其表現。所以本研究嘗試要建立出一個能專門適用於分散式資料庫導向的績效評估方法。為了要作出此績效評估方法，本研究採用了工作量模式的研究方法。先建出分散式資料庫績效評估的工作量模式，再以其來實作出績效評估方法。工作量模式分成三部分：資料模式、交易模式、控制模式。 / This thesis is intended to design a requirements-centric database benchmark, which can evaluate the general performance of the distributed relational database systems. In the past, there are many relational database benchmarks. But the relational database benchmarks like Wisconsin, AS3AP, TPC, TP1 have some constraints. In this study, we aim to design a general-purpose distributed database workload model and implement it. To design this benchmark, we need to build our workload model. The workload model consists of three components：data model, transaction model, control model. Each model has the requirement specification language to accommodate user's workloads.

With the ongoing expansion of information technology, new fields of application requiring data management emerge virtually every day. In our knowledge culture increasing amounts of data and work force organized in more creativity-oriented ways also radically change traditional fields of application and question established assumptions about data management. For instance, investigative analytics and agile software development move towards a very agile and flexible handling of data. As the primary facilitators of data management, database systems have to reflect and support these developments. However, traditional database management technology, in particular relational database systems, is built on assumptions of relatively stable application domains. The need to model all data up front in a prescriptive database schema earned relational database management systems the reputation among developers of being inflexible, dated, and cumbersome to work with. Nevertheless, relational systems still dominate the database market. They are a proven, standardized, and interoperable technology, well-known in IT departments with a work force of experienced and trained developers and administrators. This thesis aims at resolving the growing contradiction between the popularity and omnipresence of relational systems in companies and their increasingly bad reputation among developers. It adapts relational database technology towards more agility and flexibility. We envision a descriptive schema-comes-second relational database system, which is entity-oriented instead of schema-oriented; descriptive rather than prescriptive. The thesis provides four main contributions: (1)~a flexible relational data model, which frees relational data management from having a prescriptive schema; (2)~autonomous physical entity domains, which partition self-descriptive data according to their schema properties for better query performance; (3)~a freely adjustable storage engine, which allows adapting the physical data layout used to properties of the data and of the workload; and (4)~a self-managed indexing infrastructure, which autonomously collects and adapts index information under the presence of dynamic workloads and evolving schemas. The flexible relational data model is the thesis\' central contribution. It describes the functional appearance of the descriptive schema-comes-second relational database system. The other three contributions improve components in the architecture of database management systems to increase the query performance and the manageability of descriptive schema-comes-second relational database systems. We are confident that these four contributions can help paving the way to a more flexible future for relational database management technology.

With the ongoing expansion of information technology, new fields of application requiring data management emerge virtually every day. In our knowledge culture increasing amounts of data and work force organized in more creativity-oriented ways also radically change traditional fields of application and question established assumptions about data management. For instance, investigative analytics and agile software development move towards a very agile and flexible handling of data. As the primary facilitators of data management, database systems have to reflect and support these developments. However, traditional database management technology, in particular relational database systems, is built on assumptions of relatively stable application domains. The need to model all data up front in a prescriptive database schema earned relational database management systems the reputation among developers of being inflexible, dated, and cumbersome to work with. Nevertheless, relational systems still dominate the database market. They are a proven, standardized, and interoperable technology, well-known in IT departments with a work force of experienced and trained developers and administrators. This thesis aims at resolving the growing contradiction between the popularity and omnipresence of relational systems in companies and their increasingly bad reputation among developers. It adapts relational database technology towards more agility and flexibility. We envision a descriptive schema-comes-second relational database system, which is entity-oriented instead of schema-oriented; descriptive rather than prescriptive. The thesis provides four main contributions: (1)~a flexible relational data model, which frees relational data management from having a prescriptive schema; (2)~autonomous physical entity domains, which partition self-descriptive data according to their schema properties for better query performance; (3)~a freely adjustable storage engine, which allows adapting the physical data layout used to properties of the data and of the workload; and (4)~a self-managed indexing infrastructure, which autonomously collects and adapts index information under the presence of dynamic workloads and evolving schemas. The flexible relational data model is the thesis\' central contribution. It describes the functional appearance of the descriptive schema-comes-second relational database system. The other three contributions improve components in the architecture of database management systems to increase the query performance and the manageability of descriptive schema-comes-second relational database systems. We are confident that these four contributions can help paving the way to a more flexible future for relational database management technology.