Multi-objective scheduling for real-time data warehouses

The issue of write-read contention is one of the most prevalent problems when deploying real-time data warehouses. With increasing load, updates are increasingly delayed and previously fast queries tend to be slowed down considerably. However, depending on the user requirements, we can improve the response time or the data quality by scheduling the queries and updates appropriately. If both criteria are to be considered simultaneously, we are faced with a so-called multi-objective optimization problem. We transformed this problem into a knapsack problem with additional inequalities and solved it efficiently. Based on our solution, we developed a scheduling approach that provides the optimal schedule with regard to the user requirements at any given point in time. We evaluated our scheduling in an extensive experimental study, where we compared our approach with the respective optimal schedule policies of each single optimization objective

Enabling data-driven decision-making for a Finnish SME: a data lake solution

In the era of big data, data-driven decision-making has become a key success factor for companies of all sizes. Technological development has made it possible to store, process and analyse vast amounts of data effectively. The availability of cloud computing services has lowered the costs of data analysis. Even small businesses have access to advanced technical solutions, such as data lakes and machine learning applications. Data-driven decision-making requires integrating relevant data from various sources. Data has to be extracted from distributed internal and external systems and stored into a centralised system that enables processing and analysing it for meaningful insights. Data can be structured, semi-structured or unstructured. Data lakes have emerged as a solution for storing vast amounts of data, including a growing amount of unstructured data, in a cost-effective manner. The rise of the SaaS model has led to companies abandoning on-premise software. This blurs the line between internal and external data as the company’s own data is actually maintained by a third-party. Most enterprise software targeted for small businesses are provided through the SaaS model. Small businesses are facing the challenge of adopting data-driven decision-making, while having limited visibility to their own data. In this thesis, we study how small businesses can take advantage of data-driven decision-making by leveraging cloud computing services. We found that the report- ing features of SaaS based business applications used by our case company, a sales oriented SME, were insufficient for detailed analysis. Data-driven decision-making required aggregating data from multiple systems, causing excessive manual labour. A cloud based data lake solution was found to be a cost-effective solution for creating a centralised repository and automated data integration. It enabled management to visualise customer and sales data and to assess the effectiveness of marketing efforts. Better skills at data analysis among the managers of the case company would have been detrimental to obtaining the full benefits of the solution

Formal design of data warehouse and OLAP systems : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Palmerston North, New Zealand

A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily

Object reational data base management systems and applications in document retrieval

http://deepblue.lib.umich.edu/bitstream/2027.42/96902/1/MBA_JayaramanaF_1996Final.pd

Hierarchical Traffic Management of Multi-AGV Systems With Deadlock Prevention Applied to Industrial Environments

This paper concerns the coordination and the traffic management of a group of Automated Guided Vehicles (AGVs) moving in a real industrial scenario, such as an automated factory or warehouse. The proposed methodology is based on a three-layer control architecture, which is described as follows: 1) the Top Layer (or Topological Layer) allows to model the traffic of vehicles among the different areas of the environment; 2) the Middle Layer allows the path planner to compute a traffic sensitive path for each vehicle; 3) the Bottom Layer (or Roadmap Layer) defines the final routes to be followed by each vehicle and coordinates the AGVs over time. In the paper we describe the coordination strategy we propose, which is executed once the routes are computed and has the aim to prevent congestions, collisions and deadlocks. The coordination algorithm exploits a novel deadlock prevention approach based on time-expanded graphs. Moreover, the presented control architecture aims at grounding theoretical methods to an industrial application by facing the typical practical issues such as graphs difficulties (load/unload locations, weak connections,), a predefined roadmap (constrained by the plant layout), vehicles errors, dynamical obstacles, etc. In this paper we propose a flexible and robust methodology for multi-AGVs traffic-aware management. Moreover, we propose a coordination algorithm, which does not rely on ad hoc assumptions or rules, to prevent collisions and deadlocks and to deal with delays or vehicle motion errors. Note to Practitioners-This paper concerns the coordination and the traffic management of a group of Automated Guided Vehicles (AGVs) moving in a real industrial scenario, such as an automated factory or warehouse. The proposed methodology is based on a three-layer control architecture, which is described as follows: 1) the Top Layer (or Topological Layer) allows to model the traffic of vehicles among the different areas of the environment; 2) the Middle Layer allows the path planner to compute a traffic sensitive path for each vehicle; 3) the Bottom Layer (or Roadmap Layer) defines the final routes to be followed by each vehicle and coordinates the AGVs over time. In the paper we describe the coordination strategy we propose, which is executed once the routes are computed and has the aim to prevent congestions, collisions and deadlocks. The coordination algorithm exploits a novel deadlock prevention approach based on time-expanded graphs. Moreover, the presented control architecture aims at grounding theoretical methods to an industrial application by facing the typical practical issues such as graphs difficulties (load/unload locations, weak connections, ), a predefined roadmap (constrained by the plant layout), vehicles errors, dynamical obstacles, etc. In this paper we propose a flexible and robust methodology for multi-AGVs traffic-aware management. Moreover, we propose a coordination algorithm, which does not rely on ad hoc assumptions or rules, to prevent collisions and deadlocks and to deal with delays or vehicle motion errors

Adaptive Big Data Pipeline

Over the past three decades, data has exponentially evolved from being a simple software by-product to one of the most important companies’ assets used to understand their customers and foresee trends. Deep learning has demonstrated that big volumes of clean data generally provide more flexibility and accuracy when modeling a phenomenon. However, handling ever-increasing data volumes entail new challenges: the lack of expertise to select the appropriate big data tools for the processing pipelines, as well as the speed at which engineers can take such pipelines into production reliably, leveraging the cloud. We introduce a system called Adaptive Big Data Pipelines: a platform to automate data pipelines creation. It provides an interface to capture the data sources, transformations, destinations and execution schedule. The system builds up the cloud infrastructure, schedules and fine-tunes the transformations, and creates the data lineage graph. This system has been tested on data sets of 50 gigabytes, processing them in just a few minutes without user intervention.ITESO, A. C

Common ravens in Alaska's North Slope oil fields: an integrated study using local knowledge and science

Thesis (M.S.) University of Alaska Fairbanks, 2010Common ravens (Corvus corax) that nest on human structures in the Kuparuk and Prudhoe Bay oil fields on Alaska's North Slope are believed to present a predation risk to tundra-nesting birds in this area. In order to gain more information about the history of the resident raven population and their use of anthropogenic resources in the oil fields, I documented oil field worker knowledge of ravens in this area. In order to understand how anthropogenic subsidies in the oil fields affect the breeding population, I examined the influence of types of structures and food subsidies on raven nest site use and productivity in the oil fields. Oil field workers provided new and supplemental information about the breeding population. This work in conjunction with a scientific study of the breeding population suggests that structures in the oil fields were important to ravens throughout the year by providing nest sites and warm locations to roost during the winter. The breeding population was very successful and appears to be limited by suitable nest sites. The landfill is an important food source to ravens during winter, and pick-up trucks provide a supplemental source of food throughout the year. Further research will be necessary to identify how food (anthropogenic and natural) availability affects productivity and the degree to which ravens impact tundra-nesting birds.1. Introduction -- 2. An alternative information source on common ravens (Corvus corax) of Alaska's North Slope oil fields : local ecological knowledge of oil field workers -- Introduction -- This study -- Background of the oil fields and oil field workers -- Methods for documenting oil field worker knowledge -- Inverviews -- Questionnaires -- Interview and questionnaire participants : biographical details -- Focus group and individual interview content analysis -- Questionnaire analysis -- Integration of interview and questionnaire results -- Findings -- Raven population characteristics -- Raven use of the landfill, structures, dumpsters and pick-up trucks -- Raven responses to human activities -- Perspectives of ravens as predators -- Workers' perspectives and personal relationship with ravens -- Workers' perspectives on managing ravens -- Discussion -- Historical information and population change -- Winter resources, trucks, dumpsters, human activities, and ravens -- Workers personal values of ravens in the oil fields -- Future research considerations -- Acknowledgements -- Literature cited -- 3. Industrial nest ecology : common ravens in Alaska's North Slope oil fields -- Abstract -- Introduction -- Methods -- Study area -- Nest site use -- Nest characteristics -- Use of anthropogenic subsidies -- Analysis of factors affecting nest site use -- Breeding biology -- Analysis of factors affecting productivity -- Results -- Nest site use -- Nest characteristics -- Landfill use -- Factors affecting nest site use -- Breeding biology -- Factors affecting productivity -- Discussion -- Social factors and territoriality -- Use of structures and structures characteristics -- Anthropogenic food subsidies -- Nest initiation and experienced individuals -- Use of anthropogenic food subsidies by breeding adults -- Conclusions -- Acknowledgements -- Literature cited 4. Discussion and management recommendations -- Discussion -- Management recommendations -- Literature cited

Managing innovation in the real estate industry : a theory of disruptive innovations

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2000.Includes bibliographical references (leaves 101-105).Management teams in real estate firms are in a precarious position as they struggle to manage innovation without much experience in planning and executing technology-driven strategies. Real estate technology is in its infancy. The growth trajectories of innovations and the impacts of novel technologies on the future of the real estate industry have yet to be seen. This is an important time for board members and senior managers of leading real estate firms because innovation is a double-edged sword. A sound technology policy can be highly lucrative, while a failed technology strategy can prove positively fatal. This thesis studies the complexities of managing innovation in the real estate industry. It builds on the study of innovation and strategic management in other industries to provide insight into the future of the real estate industry. Managing innovation is not a new problem - there is a significant body of scholarship on the topic that has been developed through rigorous study of several industries ranging from disk drives to retailing. Researchers have produced a set of analytical frameworks and detailed case studies that explore the interaction between innovation and firm-level strategic management. This paper applies some of these analytical tools to study the nature of innovation in the real estate industry and uncover potential opportunities and pitfalls facing managers in the future.by Andre Navasargian & Tyler D. Thompson.S.M