Safety in Numbers: Developing a Shared Analytics Service for Academic Libraries

Purpose It is clear that libraries consider the use of data to inform decision making a top priority in the next five years. Jisc’s considerable work on activity data has highlighted the lack of tools and services for libraries to exploit this data. The purpose of this paper is to explore the potential of a shared analytics service for UK academic libraries and introduce the Jisc Library Analytics and Metrics Project (LAMP). The project aims to help libraries effectively management collections and services as well as delivering pre-emptive indicators and ‘actionable insights’ to help identify new trends, personalise services and improve efficiencies, economies and effectiveness (student attainment and satisfaction and institutional reputation, for example) . The project builds on the Library Impact Data Project at the University of Huddersfield and the work of the Copac Activity Data and Collections Management tools. The paper will deliver a case study of the project, its progress to date, the challenges of such an approach and the implications the service has for academic libraries. Design, methodology or approach The paper will be a case study of the project and its institutional partners and early adopters work to date and explore both the technical and cultural challenges of the work as well as its implications for the role of the library within the institution and the services it provides. Specifically the case study will comprise of the following aspects: 1. A brief history of the work and the context of library analytics services in the UK (and internationally). A description of the approach adopted by the project, and the vision and goals of the project 2. Exploration of the challenges associated with the project. In particular the challenges around accessing and sharing the data, ‘warehousing’ and data infrastructure considerations and the design challenge of visualising the data sources in a useful and coherent way 3. Outline of the implications of the project and the resultant service. In particular the implications for benchmarking (within the UK and beyond), standards development for library statistics and impact (in particular the development of ISO 16439), service development, the role of the library within the wider institution and skills and expertise of librarians. Findings This paper will report on the initial findings of the project, which will run from January 2013 to October 2013. In particular it will consider the issues surfaced through the close engagement with the academic library community (through the projects community advisory and planning group) and the institutional early-adopters around data gathering and analysis. Practical implications Data accumulated in one context has the potential to inform decisions and interventions elsewhere. While there are a number of recognised and well understood use-cases for library analytics these tend to revolve around usage and collection management. Yet, the potential of a shared analytics service is in uncovering those links and indicators across diverse data sets. The paper will consider a number of practical impacts: Performance: benchmarking, student attainment, research productivity Design: fine tuning services, personalised support Trends: research landscape, student marketplace, utilisation of resources. The case study will explore these practical implications for libraries and what they mean for the future of the library within the academy. Originality and value of the proposal The paper will present a case study of a unique service that currently fills an important gap within the library analytics space. The paper will focus on the services potential to transform both the way the library works and how it is erceived by its users, as well as its role and relationship within the broader institution

An XML-Based Streaming Concept for Business Process Execution

Service-oriented environments are central backbone of todays enterprise workflows. These workflow includes traditional process types like travel booking or order processing as well as data-intensive integration processes like operational business intelligence and data analytics. For the latter process types, current execution semantics and concepts do not scale very well in terms of performance and resource consumption. In this paper, we present a concept for data streaming in business processes that is inspired by the typical execution semantics in data management environments. Therefore, we present a conceptual process and execution model that leverages the idea of stream-based service invocation for a scalable and efficient process execution. In selected results of the evaluation we show, that it outperforms the execution model of current process engines

Middleware Architecture for Sensing as a Service

The Internet of Things is a concept that envisions the world as a smart space in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. Recent advancement in information and communication technologies make it possible to make the IoT vision a reality. However, IoT devices and consumers of data from these IoT devices can be owned by different entities which makes IoT data sharing a real challenge. Sensing as a Service is a concept that is influenced by the cloud computing term “Every Thing as a Service”. Sensing as a Service enables sensor data sharing. Sensing as a Service middleware enables IoT applications to access data generated by sensing devices owned by other entities. IoT applications are charged by the Sensing as a Service middleware for the amount of sensor data they use. This thesis addresses the architectural design of a cloud-based Sensing as Service middleware. The middleware enables sensor owners to sell their sensor data through the Internet. IoT applications can collect, and analyze sensors through the middleware API. We propose multitenancy algorithms for the middleware resource management. In addition, we propose a SQL-Like language that can be used by IoT applications for sensing service discovery, and sensor stream analytics. The evaluation of the middleware implementation shows the effectiveness of the algorithm

University of St.Gallen

For 30 years, the Institute of Information Management at the University of St. Gallen (IWI-HSG) has been dedicated to applied and design-oriented research at the intersection between business and IT. Prof. Andrea Back, Prof. Ivo Blohm, Prof. Walter Brenner, Prof. Reinhard Jung, Prof. Jan Marco Leimeister, and Prof. Robert Winter are heading six research groups covering research topics ranging from data management and analytics, design thinking, digital service innovation, to privacy and trust. We are excited to share that Prof. Thomas Grisold has joined the University of St.Gallen as an Assistant Professor this year

MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service

Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data

SLA-aware operational efficiency in AI-enabled service chains: challenges ahead

Service providers compose services in service chains that require deep integra tion of core operational information systems across organizations. Additionally, advanced analytics inform data-driven decision-making in corresponding AI-ena-bled business processes in today’s complex environments. However, individual partner engagements with service consumers and providers often entail individu-ally negotiated, highly customized Service Level Agreements (SLAs) comprising engagement-specific metrics that semantically differ from general KPIs utilized on a broader operational (i.e., cross-client) level. Furthermore, the number of unique SLAs to be managed increases with the size of such service chains. The resulting complexity pushes large organizations to employ dedicated SLA management sys-tems, but such ‘siloed’ approaches make it difficult to leverage insights from SLA evaluations and predictions for decision-making in core business processes, and vice versa. Consequently, simultaneous optimization for both global operational process efficiency and engagement-specific SLA compliance is hampered. To address these shortcomings, we propose our vision of supplying online, AI-supported SLA analyt-ics to data-driven, intelligent core workflows of the enterprise and discuss current research challenges arising from this vision. Exemplified by two scenarios derived from real use cases in industry and public administration, we demonstrate the need for improved semantic alignment of heavily customized SLAs with AI-enabled operational systems. Moreover, we discuss specific challenges of prescriptive SLA analytics under multi-engagement SLA awareness and how the dual role of AI in such scenarios demands bidirectional data exchange between operational processes and SLA management. Finally, we discuss the implications of federating AI-sup-ported SLA analytics across organizations

A framework for orchestrating secure and dynamic access of IoT services in multi-cloud environments

IoT devices have complex requirements but their limitations in terms of storage, network, computing, data analytics, scalability and big data management require it to be used it with a technology like cloud computing. IoT backend with cloud computing can present new ways to offer services that are massively scalable, can be dynamically configured, and delivered on demand with largescale infrastructure resources. However, a single cloud infrastructure might be unable to deal with the increasing demand of cloud services in which hundreds of users might be accessing cloud resources, leading to a big data problem and the need for efficient frameworks to handle a large number of user requests for IoT services. These challenges require new functional elements and provisioning schemes. To this end, we propose the usage of multi-clouds with IoT which can optimize the user requirements by allowing them to choose best IoT services from many services hosted in various cloud platforms and provide them with more infrastructure and platform resources to meet their requirements. This paper presents a novel framework for dynamic and secure IoT services access across multi-clouds using cloud on-demand model. To facilitate multi-cloud collaboration, novel protocols are designed and implemented on cloud platforms. The various stages involved in the framework for allowing users access to IoT services in multi-clouds are service matchmaking (i.e. to choose the best service matching user requirements), authentication (i.e. a lightweight mechanism to authenticate users at runtime before granting them service access), and SLA management (including SLA negotiation, enforcement and monitoring). SLA management offers benefits like negotiating required service parameters, enforcing mechanisms to ensure that service execution in the external cloud is according to the agreed SLAs and monitoring to verify that the cloud provider complies with those SLAs. The detailed system design to establish secure multi-cloud collaboration has been presented. Moreover, the designed protocols are empirically implemented on two different clouds including OpenStack and Amazon AWS. Experiments indicate that proposed system is scalable, authentication protocols result only in a limited overhead compared to standard authentication protocols, and any SLA violation by a cloud provider could be recorded and reported back to the user.N/