Scalability of findability: decentralized search and retrieval in large information networks

Amid the rapid growth of information today is the increasing challenge for people to survive and navigate its magnitude. Dynamics and heterogeneity of large information spaces such as the Web challenge information retrieval in these environments. Collection of information in advance and centralization of IR operations are hardly possible because systems are dynamic and information is distributed. While monolithic search systems continue to struggle with scalability problems of today, the future of search likely requires a decentralized architecture where many information systems can participate. As individual systems interconnect to form a global structure, finding relevant information in distributed environments transforms into a problem concerning not only information retrieval but also complex networks. Understanding network connectivity will provide guidance on how decentralized search and retrieval methods can function in these information spaces. The dissertation studies one aspect of scalability challenges facing classic information retrieval models and presents a decentralized, organic view of information systems pertaining to search in large scale networks. It focuses on the impact of network structure on search performance and investigates a phenomenon we refer to as the Clustering Paradox, in which the topology of interconnected systems imposes a scalability limit. Experiments involving large scale benchmark collections provide evidence on the Clustering Paradox in the IR context. In an increasingly large, distributed environment, decentralized searches for relevant information can continue to function well only when systems interconnect in certain ways. Relying on partial indexes of distributed systems, some level of network clustering enables very efficient and effective discovery of relevant information in large scale networks. Increasing or reducing network clustering degrades search performances. Given this specific level of network clustering, search time is well explained by a poly-logarithmic relation to network size, indicating a high scalability potential for searching in a continuously growing information space

Enhancing systems integration by incorporating business continuity drivers

Purpose – The purpose of this paper is to present a framework for developing an integrated operating environment (IOE) within an enterprise information system by incorporating business continuity drivers. These drivers enable a business to continue with its operations even if some sort of failure or disaster occurs. Design/methodology/approach – Development and implementation of the framework are based on holistic and top-down approach. An IOE on server’s side of contemporary business computing is investigated in depth. Findings – Key disconnection points are identified, where systems integration technologies can be used to integrate platforms, protocols, data and application formats, etc. Downtime points are also identified and explained. A thorough list of main business continuity drivers (continuous computing (CC) technologies) for enhancing business continuity is identified and presented. The framework can be utilized in developing an integrated server operating environment for enhancing business continuity. Originality/value – This paper presents a comprehensive framework including exhaustive handling of enabling drivers as well as disconnection points toward CC and business continuity

New Prospects for Organizational Democracy? How the Joint Pursuit of Social and Financial Goals Challenges Traditional Organizational Designs

Some interesting exceptions notwithstanding, the traditional logic of economic efficiency has long favored hierarchical forms of organization and disfavored democracy in business. What does the balance of arguments look like, however, when values besides efficient revenue production are brought into the picture? The question is not hypothetical: In recent years, an ever increasing number of corporations have developed and adopted socially responsible behaviors, thereby hybridizing aspects of corporate businesses and social organizations. We argue that the joint pursuit of financial and social objectives warrants significant rethinking of organizational democracy’s merits compared both to hierarchy and to non-democratic alternatives to hierarchy. In making this argument, we draw on an extensive literature review to document the relative lack of substantive discussion of organizational democracy since 1960. And we draw lessons from political theory, suggesting that the success of political democracy in integrating diverse values offers some grounds for asserting parallel virtues in the business case

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Impact of Cloud Computing on Business Process Outsourcing - Case: Accounting in Small and Medium-sized Enterprises

Cloud computing is a model for providing on-demand access to a shared pool of computational resources in a cost-efficient and convenient manner, involving minimal interaction with the cloud provider. Within the last eight years cloud computing has evolved from a promising, emerging technology to a credible alternative for fulfilling organizations' IT needs. Previous research has addressed a variety of issues including cloud sourcing and implementation in organizations. However, there is a gap in our understanding when it comes to the implications of cloud-based information systems on business process outsourcing (BPO). The objective of this dissertation is to contribute to the understanding of how the introduction of cloud-based-information systems affects BPO arrangements. The context of this research is professional business-to-business (B2B) services outsourcing by small and medium-sized enterprises (SME). This setting is depicted as an outsourcing triangle, which includes a client company, a professional service provider, and a cloud-based information system. The four essays included in this dissertation investigate the implications of cloud computing from the perspective of the three sides of the triangle. The first essay seeks to understand factors behind cloud computing adoption in organizations. The second and third essays investigate the outsourcing patterns of client companies and BPO decision-making in the context of cloud-based information systems. Finally, the fourth essay addresses changes in the organization of professional service providers. The main theoretical contributions of the dissertation include (1) a revised cloud computing adoption framework, (2) a conceptualization of the outsourcing continuum, (3) an enhanced understanding of transaction costs in the cloud context, and (4) a framework of virtual organization for professional B2B service providers. For practitioners, this dissertation offers a set of guidelines for the implementation of cloud-based information systems in BPO arrangements, and the reorganization of work to suit the technology

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

When management encounters complexity

This paper aims at showing how management has come to encounter the sciences of complexity. Therefore the various levels and domains of management are outlined which leverage from the study of complexity. This is not, however, a descriptive study. Rather, we focus on how management can benefit from knowing of the sciences of complexity. New tools and rods, new languages and approaches are sketched that show a radical shift in management leading from a once dependent discipline from physics and engineering, towards a biologically and ecologically permeated new management.Whereas the main concern for complexity consists in understanding complex phenomena and systems, at the end a number of successful applications of complexity to management and entrepreneurial consulting are considered

Quality of Service Aware Data Stream Processing for Highly Dynamic and Scalable Applications

Huge amounts of georeferenced data streams are arriving daily to data stream management systems that are deployed for serving highly scalable and dynamic applications. There are innumerable ways at which those loads can be exploited to gain deep insights in various domains. Decision makers require an interactive visualization of such data in the form of maps and dashboards for decision making and strategic planning. Data streams normally exhibit fluctuation and oscillation in arrival rates and skewness. Those are the two predominant factors that greatly impact the overall quality of service. This requires data stream management systems to be attuned to those factors in addition to the spatial shape of the data that may exaggerate the negative impact of those factors. Current systems do not natively support services with quality guarantees for dynamic scenarios, leaving the handling of those logistics to the user which is challenging and cumbersome. Three workloads are predominant for any data stream, batch processing, scalable storage and stream processing. In this thesis, we have designed a quality of service aware system, SpatialDSMS, that constitutes several subsystems that are covering those loads and any mixed load that results from intermixing them. Most importantly, we natively have incorporated quality of service optimizations for processing avalanches of geo-referenced data streams in highly dynamic application scenarios. This has been achieved transparently on top of the codebases of emerging de facto standard best-in-class representatives, thus relieving the overburdened shoulders of the users in the presentation layer from having to reason about those services. Instead, users express their queries with quality goals and our system optimizers compiles that down into query plans with an embedded quality guarantee and leaves logistic handling to the underlying layers. We have developed standard compliant prototypes for all the subsystems that constitutes SpatialDSMS

Knowledge Discovery and Data Mining (KDDM) survey report.

The large number of government and industry activities supporting the Unit of Action (UA), with attendant documents, reports and briefings, can overwhelm decision-makers with an overabundance of information that hampers the ability to make quick decisions often resulting in a form of gridlock. In particular, the large and rapidly increasing amounts of data and data formats stored on UA Advanced Collaborative Environment (ACE) servers has led to the realization that it has become impractical and even impossible to perform manual analysis leading to timely decisions. UA Program Management (PM UA) has recognized the need to implement a Decision Support System (DSS) on UA ACE. The objective of this document is to research the commercial Knowledge Discovery and Data Mining (KDDM) market and publish the results in a survey. Furthermore, a ranking mechanism based on UA ACE-specific criteria has been developed and applied to a representative set of commercially available KDDM solutions. In addition, an overview of four R&D areas identified as critical to the implementation of DSS on ACE is provided. Finally, a comprehensive database containing detailed information on surveyed KDDM tools has been developed and is available upon customer request