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許多組織為了建立競爭優勢，運用團隊成員的專業及經驗去解決問題，創造差異化的產品。這個趨勢可以解釋為何團隊的研究目前又開始熱門，特別是團隊知識歷程。“交融記憶系統”（TMS） 就是其中一個著重在瞭解團隊知識歷程的理論架構。“交融記憶系統”的構念，特別強調如何利用和組合分散在個人身上的專業，以幫助我們瞭解知識任務者的團隊如何充分發揮個別成員知識的價值（Lewis,2003） 。透過團隊的交融記憶系統，團隊成員可以建立各自專業，信賴且有效溝通，這似乎正是解決目前產業問題的良方。 Lewis（2003）為了在實務上有效應用交融記憶系統的理論，到各種不同目的與型態的團隊，編制了交融記憶系統量表。這個量表包括“專業”（Specialization）、“信賴”（Credibility）、與“協調”（Coordination）三個分量表，每個分量表各有五個題目，整個量表共有15題的5點李克特式量表。以其研究物件的團隊 來看，此量表可廣泛應用于研發創新團隊、中小型企業與新創公司等，幫助公司預估績效與提高競爭力。 本研究分為兩大部分。第一部分為量化研究，對Lewis（2003）所編量表進行修訂，以用來測量臺灣地區團隊的團隊交融記憶系統：為了驗證本量表效度，本研究收集了大量企業及學生團隊樣本，以統計方法分析績效良好團隊在交融記憶系統量表分數，是否顯著高於績效不彰團隊外，也分析交融記憶系統量表與團隊成員“依附風格” 、“創新行為”、“團隊創意觀念產生”、“成就目標” 之間的相關關係，以確認量表的建構效度。第二部份研究為質性研究，透過對實務團隊的深入訪談與實證，驗證交融記憶系統在成效良好團運作的現況。 本研究第一個成果在成功修訂交融記憶系統量表。修訂後的量表在統計分析後，績效良好團隊的交融記憶系統量表分數，顯著高於績效不彰團；和“依附風格”、“創新行為”、“團隊創意觀念產生”、“成就目標”之間的相關性，也和理論原始架構相符，證實了量表的信效度。本量表將可有效衡量臺灣各式團隊的交融記憶系統。 本研究的第二個成果，透過實務團隊的訪談與實證，驗證了交融記憶系統在國內成效良好團隊運作的現況。文化創意產業的紙風車兒童劇團 ，及TIC100創業競賽的冠軍團隊 ，這兩個成功典範團隊的運作中，雖然成員本身沒有認知到系統的存在，不過都有運作良好的團隊交融記憶系統，再度確認交融記憶系統理論架構在實務上團隊中的運作。 筆者透過成功典範的訪談與適合臺灣量表的建立，希望能做為業界建立與運作團隊時的參考，而對團隊的成功運作有所幫助。 關鍵字：交融記憶系統、團隊、研發、創造力

Responsive analytics are rapidly taking over the traditional data analytics dominated by the post-fact approaches in traditional data warehousing. Recent advancements in analytics demand placing analytical engines at the forefront of the system to react to updates occurring at high speed and detect patterns, trends, and anomalies. These kinds of solutions find applications in Financial Systems, Industrial Control Systems, Business Intelligence and on-line Machine Learning among others. These applications are usually associated with Big Data and require the ability to react to constantly changing data in order to obtain timely insights and take proactive measures. Generally, these systems specify the analytical results or their basic elements in a query language, where the main task then is to maintain query results under frequent updates efficiently. The task of reacting to updates and analyzing changing data has been addressed in two ways in the literature: traditional business intelligence (BI) solutions focus on historical data analysis where the data is refreshed periodically and in batches, and stream processing solutions process streams of data from transient sources as flows of data items. Both kinds of systems share the niche of reacting to updates (known as dynamic evaluation), however, they differ in architecture, query languages, and processing mechanisms. In this thesis, we investigate the possibility of a reactive and unified framework to model queries that appear in both kinds of systems.In traditional BI solutions, evaluating queries under updates has been studied under the umbrella of incremental evaluation of queries that are based on the relational incremental view maintenance model and mostly focus on queries that feature equi-joins. Streaming systems, in contrast, generally follow automaton based models to evaluate queries under updates, and they generally process queries that mostly feature comparisons of temporal attributes (e.g. timestamp attributes) along with comparisons of non-temporal attributes over streams of bounded sizes. Temporal comparisons constitute inequality constraints while non-temporal comparisons can either be equality or inequality constraints. Hence these systems mostly process inequality joins. As a starting point for our research, we postulate the thesis that queries in streaming systems can also be evaluated efficiently based on the paradigm of incremental evaluation just like in BI systems in a main-memory model. The efficiency of such a model is measured in terms of runtime memory footprint and the update processing cost. To this end, the existing approaches of dynamic evaluation in both kinds of systems present a trade-off between memory footprint and the update processing cost. More specifically, systems that avoid materialization of query (sub)results incur high update latency and systems that materialize (sub)results incur high memory footprint. We are interested in investigating the possibility to build a model that can address this trade-off. In particular, we overcome this trade-off by investigating the possibility of practical dynamic evaluation algorithm for queries that appear in both kinds of systems and present a main-memory data representation that allows to enumerate query (sub)results without materialization and can be maintained efficiently under updates. We call this representation the Dynamic Constant Delay Linear Representation (DCLRs).We devise DCLRs with the following properties: 1) they allow, without materialization, enumeration of query results with bounded-delay (and with constant delay for a sub-class of queries), 2) they allow tuple lookup in query results with logarithmic delay (and with constant delay for conjunctive queries with equi-joins only), 3) they take space linear in the size of the database, 4) they can be maintained efficiently under updates. We first study the DCLRs with the above-described properties for the class of acyclic conjunctive queries featuring equi-joins with projections and present the dynamic evaluation algorithm called the Dynamic Yannakakis (DYN) algorithm. Then, we present the generalization of the DYN algorithm to the class of acyclic queries featuring multi-way Theta-joins with projections and call it Generalized DYN (GDYN). We devise DCLRs with the above properties for acyclic conjunctive queries, and the working of DYN and GDYN over DCLRs are based on a particular variant of join trees, called the Generalized Join Trees (GJTs) that guarantee the above-described properties of DCLRs. We define GJTs and present algorithms to test a conjunctive query featuring Theta-joins for acyclicity and to generate GJTs for such queries. We extend the classical GYO algorithm from testing a conjunctive query with equalities for acyclicity to testing a conjunctive query featuring multi-way Theta-joins with projections for acyclicity. We further extend the GYO algorithm to generate GJTs for queries that are acyclic.GDYN is hence a unified framework based on DCLRs that enables processing of queries that appear in streaming systems as well as in BI systems in a unified main-memory model and addresses the space-time trade-off. We instantiate GDYN to the particular case where all Theta-joins involve only equalities and inequalities and call this instantiation IEDYN. We implement DYN and IEDYN as query compilers that generate executable programs in the Scala programming language and provide all the necessary data structures and their maintenance and enumeration methods in a continuous stream processing model. We evaluate DYN and IEDYN against state-of-the-art BI and streaming systems on both industrial and synthetically generated benchmarks. We show that DYN and IEDYN outperform the existing systems by over an order of magnitude efficiency in both memory footprint and update processing time. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished