Linh Manh Pham

Ahmed El Rheddane

Didier Donsez

Noel De Palma

Ahmed El-Rheddane

CIRUS: an elastic cloud-based framework for Ubilytics

Keywords: Big Data analytics, Cloud computing, Elasticity, Internet of Things, Middleware, Ubilytics

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Introduction

The Internet of Things (IoT) [START_REF] Sundmaeker | Vision and Challenges for Realising the Internet of Things[END_REF] has become a reality with the availability of chatty embedded devices such as RFID, wireless sensors, mobile sensors, personal smartphones, etc. According to IDC, the installed base of the Internet of Things will be approximately 212 billion "things" globally by the end of 2020 [START_REF]IDC, Worldwide and Regional Internet of Things (IoT) 2014-2020 Forecast: A Virtuous Circle of Proven Value and Demand[END_REF]. IoT provides companies with new opportunities of economic models (e.g. pay as you go), improves the quality of service delivered to their customers both individuals and companies and helps them to satisfy their legal and contractual duties. The IoT is now an established efficiency and productivity tool for agile enterprises and organizations. However, associated services called Machine to Machine (i.e. M2M) require the analysis of a huge amount of data produced by swarms of sensors and collected by dedicated gateways. This huge amount of IoT data must be analysed with models (e.g. Map-Reduce, ESP, CEP [START_REF] Manyika | Big data: The next frontier for innovation, competition, and productivity[END_REF]) and technologies (e.g. Hadoop, Storm [START_REF] Storm | [END_REF]) of the "Big Data Analytics" (BDA for short), deployed on Cloud computing platforms [START_REF] Llorente | Key Challenges in Cloud Computing to Enable Future Internet of Things[END_REF]. This new field is named Ubilytics (ubiquitous big data analytics) by [START_REF] Elmqvist | Ubiquitous Analytics: Interacting with Big Data Anywhere, Anytime[END_REF]. Currently, deploying Ubilytics infrastructures/projects often requires engineers with skills in various technologies of Cloud computing, Big Data, IoT as well as diverse business domains such as smart grid, supply chain, healthcare, etc. Gartner forecasts that the business behind Big Data will globally create 4.4 million IT jobs by 2015 with the mobile and social networks [START_REF]Gartner says big data creates big jobs: 4.4 million IT jobs globally to[END_REF]. Ubilytics will create a new kind of IT job, the Ubilylist. Ubilylists are business and domain experts who are able to design and deploy complex analytic workflows and to interpret ubiquitous big data results. Whereas, deploying and configuring (i.e. D&C process) the infrastructure for Ubilytics is a complex task and requires several IT skills to deal with various technologies from embedded IoT gateways to cloud-based analytic platforms.

The CIRUS framework is a generic and elastic cloud-based Platform as a service (PaaS) for real-time Ubilytics. The CIRUS project targets the ubilylists who are not Cloud D&C expert. It allows them to design complex workflows of standard analytics, sensor data sources and data viz interfaces as well as to deploy these components both on the IoT gateways and on virtual servers hosted by one or several Cloud platforms. Moreover, the CIRUS PaaS aims to design and deploy rapidly Software as a service (SaaS) for Ubilytics. In summary, we make the following contributions in this paper: [START_REF] Sundmaeker | Vision and Challenges for Realising the Internet of Things[END_REF] We propose the novel architecture of CIRUS, a generic and elastic cloud-based PaaS platform for Ubilytics using Component-off-the-Shelves (COTS); [START_REF]IDC, Worldwide and Regional Internet of Things (IoT) 2014-2020 Forecast: A Virtuous Circle of Proven Value and Demand[END_REF] We perform an experiment which validates the genericity and elasticity of CIRUS using a practical Ubilytics use case. This experiment is deployed on a hybrid Cloud environment consisting of a private VMware vSphere [START_REF][END_REF] hosting center, the public Amazon EC2 [START_REF]Amazon EC2[END_REF] and Microsoft Azure [START_REF]Microsoft Azure[END_REF] Clouds.

The paper is organized as follows. Section 2 presents the related work and motivation. Section 3 describes the overall architecture and components of CIRUS. The proof-of-concept implementation of CIRUS using a dataset from practical sources is demonstrated in an use case of Section 4. Section 5 uses this use case in an experiment to validate the proposed framework. Conclusions are stated in Section 6.

Related work and motivation

Since CIRUS framework is a flexible cloud-based PaaS platform for Ubilytics, we compare it with cloud-based BDA platforms for IoT.

On this aspect, several research works target special IoT application domains. Ma et al. [START_REF] Ma | An efficient index for massive IOT data in cloud environment[END_REF] integrate the on-demand ability of the Cloud to their Big Data framework for IoT. In order to deal with the large volume of IoT data, they propose an update and query efficient index framework (UQE-Index) based on key-value store that can support high insert throughput and provide efficient multi-dimensional query simultaneously. In [START_REF] Talia | Clouds for Scalable Big Data Analytics[END_REF], Talia introduces Data Mining Cloud Framework as a Data Analytic (DAPaaS), a programming environment for development of BDA applications, including supports for single-task and parameter-sweeping data mining programs. A cloud-based platform for BDA in power grid, validated in a micro-grid at USC campus, has been proposed by Simmhan,et al. [17]. This platform provides a pipeline mechanism to integrate information from various sources, digest and store them in static repositories for sharing knowledge. These approaches do not mention the D&C process of such complex Big Data frameworks. It is an error-prone and complicated work for the ubilylists, which needs to be automated by intermediate systems or middlewares like CIRUS does.

Only a few projects such as Globus Provision [START_REF] Liu; Sotomayor | Deploying Bioinformatics Workflows on Clouds with Galaxy and Globus Provision[END_REF] and CloudMan [START_REF] Afgan | Galaxy CloudMan: delivering cloud compute clusters[END_REF] take the issue of automating deployment of BDA applications on Cloud infrastructure into consideration. Globus Provision is a cloud resource management tool for deploying biologic computational Grids on EC2. It offers tools such as CRData and Globus Transfer to deal with massive dataset on the Cloud. CloudMan is also a Cloud resource management tool for automation of the deployment of a Galaxy instance on EC2. However, these two frameworks are dedicated to one specific Cloud and used for only one type of BDA applications. In contrast to CloudMan and Globus Provision, Jin, et al. [START_REF] Jin | Automating Deployment of Customized Scientific Data Analytic Environments on Clouds[END_REF] propose an automatic deployment framework which supports heterogeneous environments for deployment of BDA applications. However, it only supports static deployment, the elasticity is not considered at all. Elasticity is one of the major benefits of Cloud computing to add quality of service (QoS) into cloud services [START_REF] Coutinho | Elasticity in cloud computing: a survey[END_REF]. To the best of our knowledge, CIRUS is the first preliminary framework providing automated D&C and elasticity for cloud-based BDA applications.

Along with academic efforts, commercial Big Data as-a-Service (BDaaS) solutions for IoT are currently available and built on open-source platforms. Splunk Cloud [START_REF] Splunkcloud | [END_REF] is a proprietary cloud-based service that consumes, searches and visualizes different sources of machine data. This multi-tenant DA SaaS model provides analysing and monitoring suites for developers, application support staffs, system administrators, and security analysts who have to deal with a bulk of data every single day. Amazon Kinesis offers real-time processing, scalable data throughput and volume which accommodate up to "hundreds of terabytes of data per hour from hundreds of thousands of sources" [START_REF] Kinesis | [END_REF]. However, those solutions are not portable from one provider to another. Interoperability, portability and hybrid cloud configuration are seriously compromised. Moreover, although these Big Data IoT systems are implemented and orchestrated automatically on the Cloud, they do not take into account the dynamic reaction to changes from surrounding environment.

In comparison with the state-of-the-art, the CIRUS project aims to provide a generic and elastic PaaS Cloud infrastructure for BDA, which digests IoT Fig. 1: The CIRUS novel framework implementing lambda architecture data and covers the automated D&C process of software components on both IoT gateways and Cloud machines.

The CIRUS framework

The CIRUS framework is designed as a flexible architecture described as a set of abstract components. Each of these abstract components will be specialized into concrete components when a CIRUS application is deployed on the Cloud. Therefore the platform assembly can be replaced easily to use new IoT gateway protocols, message brokers and BDA platforms. The overall novel architecture of the CIRUS framework is depicted in Fig. 1. As shown in this figure, CIRUS is a combination of 3 application tiers which are IoT edges, Message brokers and BDA platforms. The BDA platform tier, in turn, is divided into 3 main layers (serving, batch and speed) according to Lambda architecture [START_REF]Lambda Architecture: A state-of-the-art[END_REF] which is a data-processing architecture designed to deal with massive data from multiple sources. A lambda system contains typically three layers: batch processing aiming to perfect accuracy, speed (or real-time stream processing) for minimizing latency, and a serving layer for responding to queries. Following is details about main components of CIRUS.

IoT Edge tier is deployed on the IoT gateways. It collects data from various sensor networks (e.g. enOcean, Zigbee, etc.) and publish them to the brokers. They store temporally the data when the network such as Wifi public hotspots or ADSL is not available. Gateways can be either mobile (from people, cars to sounding balloons) or static (vending machines, interactive digital signages). Message Brokers tier implements the distributed publishsubscribe patterns with various QoS properties. These include fault-tolerance, high-availability, elasticity, causality, deterministic delivery time, etc. BDA Platforms tier implements various BDA solutions, which comprises of following three layers:

-Data Storage operates at Serving layer to store temporal data of sensors as well as calculated aggregation data and prediction models. -Batch processor operating at Batch layer is to retrieve offline data from the storages and perform batch processing. -Event stream processor operating at Speed layer aims to analyse in real-time the stream of sensor data incoming from the IoT edges thanks to the brokers to classify the data. Machine Learning processor situated between batch and speed layers is to calculate unsupervised models of offline big data and parameterize the event stream processing (i.e ESP).

DSL-based plug-ins allows to describe Big Data applications in terms of abstract components that vary from the sensors to use, the clouds to implement to the topologies to reuse, etc. D&C manager parses the application descriptions to concrete instances which can be deployed, configured, adapted automatically in a hybrid cloud infrastructure and on physical machines (gateways) at runtime. The D&C manager allows third-party monitoring and scaling plug-ins to be integrated in its core. This component monitors the three lambda layers and makes scaling decisions based on variations of environment that enables the elasticity for the framework.

Based on the flexible architecture which is a composition of many COTS components, CIRUS can implement and (self-)manage any kind of BDA applications. As a first step towards validating the genericity and elasticity of CIRUS, we describe in details its selected components and data-flow processing in a smart-grid use case. CIRUS allows to use OTSO (Off-the-shelf Option) [START_REF] Kontio | A Case Study in Applying a Systematic Method for COTS Selection[END_REF] and CAP (COTS Acquisition Process) [START_REF] Ochs | A Method for Efficient Measurement based COTS Assessment and Selection -Method Description and Evaluation Results[END_REF] as COTS selection methods. With the smart-grid domain, we use CAP, which considers domain/domain compatibility criterion, to select appropriate components among many candidates for each tier as well as layer of CIRUS.

The smart-grid use case

For validating our Ubilytics PaaS, we have implemented a prototype of CIRUS for simple smart-grid application in which the electricity supplier can forecast the electric load in the next minutes by analysing in real time electric consumptions collected in houses. In the short term, the infrastructure will be adjusted dynamically according to the load of sensor messages' throughput by an autonomous manager such as Roboconf [START_REF] Pham | Roboconf: A Hybrid Cloud Orchestrator to Deploy Complex Applications[END_REF]. For instance, additional cloud Fig. 2: The CIRUS's components for the smart-grid use case instances can be added to the speed layer of the architecture when workload goes over a threshold. The detailed architecture of the CIRUS prototype for the use case is shown in Fig. 2. As the batch processing layer is not necessary in this particular use case, without loss of generality, we have not implemented it in the prototype. The components of the smart-grid use case are described as follows.

IoT gateways

For the sensor data collection, we have chosen the OpenHAB framework 1 which provides an integration platform for sensors and actuators of the home automation. OpenHAB allows users to specify DSL-based rules which will be parsed by its rule engine to update the actuator's commands upon sensor's state changes. The Event-Condition-Action (ECA) paradigm is used by Open-HAB for executing the home automation actions. ECA rules are triggered on sensor value changes, command emission and timer expiration. Events (e.g. state changes and commands) can be "imported" or "exported" using bindings for MQTT, XMPP, Twitter, etc. OpenHAB can be installed and run on embedded boards, some of which are Raspberry Pi, Beaglebone Black and Intel Galileo. For the smart-grid use case, we have developed a new OpenHAB plug-in (called binding) in order to replay the sensor log files containing the smart-plug measurements (e.g. timestamped load and work) of each house. It is deployed on both embedded boards and virtual machines (VMs) on the Cloud with one instance per house. Data from OpenHAB is sent to the MQTT brokers.

MQTT brokers

Message Queue Telemetry Transport (MQTT) [START_REF]MQ Telemetry Transport[END_REF] is a transport data protocol for M2M networks. It is devised for supporting low-bandwidth and unreliable networks, as illustrated by satellite links or sensor networks. MQTT follows the publish-subscribe pattern between sensors and one or more sinks like M2M gateways, etc. Mosquitto [START_REF] Mosquitto | [END_REF] and RabbitMQ [START_REF] Rabbitmq | [END_REF] are the main robust and open-source implementations of MQTT brokers that we have selected for this use case. The data from sensors are classified in the brokers according to specific topics (e.g. houses or plugs) and then delivered to the Speed layer.

Speed layer for real-time analytics

For the speed layer of the lambda architecture, we have chosen the Apache Storm framework. Storm is a real-time ESP system. It is designed to deploy a processing chain in a distributed infrastructure such as a Cloud platform (IaaS). For the Ubilytics platform, we have developed a new Storm input components (called spout) in order to generate sensor tuples from the MQTT brokers by subscribing on the MQTT topics with one spout per house.

Historical data storage

In the speed layer, the Storm topology needs to maintain some execution ongoing state. This is the case for the sliding window average of sensor values. To do this we use Storm with Cassandra [START_REF]Apache Cassandra[END_REF] for our real-time power consumption prediction. Cassandra is an open source distributed database management system (NoSQL solution). It is created to handle large amounts of data spread out across many nodes, while providing a highly available service with no single point of failure. Cassandra's data model allows incremental modifications of rows.

D&C manager

As mentioned, Roboconf is our choice for the elastic D&C manager in multi-Clouds. It is a middleware for autonomously configuring, installing, and managing complex legacy application stacks deployed on the cloud VMs and on Fig. 3: Components of the smart-grid use case under Roboconf's DSL physical machines which dynamically evolve over time. Following the Service-Oriented Architecture (SOA) principles, Roboconf consists of a simple DSL for describing configuration properties and inter-dependencies of service components; a distributed configuration protocol ensuring the dynamic resolution of these inter-dependencies; a runtime system that guarantees the correct (re)deployment and management of distributed application across multiple Clouds, physical devices or virtual machines. Roboconf supports popular IaaS Cloud platforms such as EC2, Azure and vSphere. The configuration of components in smart-grid use case under Roboconf's DSL are excerpted and shown in Fig. 3.

This section describes the prototype for the smart-grid use case which serves as a proof-of-concept for the genericity of CIRUS. However, to validate the elasticity, an experiment in an autonomous environment needs to be conducted.

Validating experiments

In order to evaluate the elasticity of the proposed framework, we use the smart-grid prototype in implementing an experiment which worker instances of "Event stream processor" component (the Storm cluster in this situation) will be scaled out/in depending on fluctuation of workload. The experiment is deployed on a hybrid Cloud environment. Each house is represented by an OpenHAB process which publishes a given dataset related to the house (approximately 2.5 GB per house). The dataset files, which are derived from DEBS 2014 Grand Challenge [START_REF] Debs Gc | [END_REF], are preloaded on microSD cards. The dataset is based on practical records collecting from smart plugs, which are deployed in private households of 40 houses located in Germany. Those data are collected Fig. 4: Instances of components of the smart-grid use case under Roboconf's DSL roughly every second for each sensor in each smart plug. It is worth noting that the data set is gathered in an real-world, uncontrolled environment, which implies the possibility of producing imperfect data and measurements.

The OpenHAB gateways are deployed on Beaglebone Black [START_REF] Black | [END_REF] embedded boards. The aggregated data and results are finally stored in an Apache Cassandra database which is in our vSphere private Cloud for safety. The cluster of MQTT brokers, containing the topics, are hosted on EC2 public Cloud VMs. The Storm cluster contains worker nodes, each corresponding to a VM instance on Azure Cloud for taking advantage of the computing strength of our granted Microsoft infrastructure. The dynamic D&C process is performed automatically by Roboconf. Fig. 4 shows a short extract about instances of the experiment under Roboconf's DSL.

Storm topology for real-time load forecasting

To handle the consumption prediction query in a continuous and scalable manner we use a Storm-based solution. The elaborations consist of queries that are continuously evaluated on the events supplied as input. Storm cluster is composed of two kinds of nodes: a master node which runs a daemon called Nimbus and worker ones which run a daemon called Supervisor. The Nimbus is responsible for distributing code around the cluster, assigning tasks to machines and monitoring for failures. Each supervisor listens for works assigned to its node, it starts and stops worker processes when necessary based on what Nimbus has assigned to it. Each worker process executes a subset of a topology; a running topology may consist of many worker processes spread across many nodes.

Monitoring and scaling decision

We have developed a Ganglia [START_REF] Ganglia | [END_REF] plug-in for Roboconf to monitor the topology and fetch the metrics that we consider relevant to characterize the load of a given component. Moreover, Storm also exposes application-metrics, such as the number of emitted or executed tuples. We consider that these Storm metrics are the most fit to reflect the load of a Storm component. Basically, the state of a component is described by the difference between the tuples it receives, i.e., the tuples that are emitted by all its input components, and the tuples it is able to process within a given period of time. While this is straight forward to compute, it takes into account virtually any type of bottlenecks a component might encounter: should the processing of a tuple be CPU-intensive or should it rely on an external web service or database connection, it would invariably tell if a given component is able to keep up with the pace of its incoming stream of data.

Once we have the Storm metrics of the components and provided that load balancing among different instances of the same component is guaranteed, through a random policy for instance, the scaling decision goes as follows:

Before scaling out, we store the last witnessed capacity of the component's instances as described by equation ( 2). This will be later used to initiate scaling in.

capacity = i<n executed i n (2)
-Scaling in: which is the removal of an unnecessary component instance, should take place if we can guarantee that, with one instance less, the component's instances would still not be overloaded. Using the previously stored capacity, that is updated upon each scaling out, scaling in is instigated upon formula (3) verification.

inputs emitted > (n -1)capacity

Obviously, changing the number of component instances would not have the desired effect on performance unless it is coupled with a correspondent change in the amount of provisioned resources.

Result discussion

This experiment shows how our framework provides elasticity for BDA applications in multi-Clouds using the prototype for smart-grid use case. The initial cluster is set so as to have one worker per component of our topology. The incoming throughput is then varied gradually in order to see its impact on the behaviour of our topology.

Figures 6 and7 show the results of this experiment, components that have not needed scaling have been omitted. We can see that at times, there is a decrease in the number of emitted bolts (Fig. 6). This is detected by our elasticity controller which instigate the corresponding scaling operation (Fig. 7). The pikes following each scaling are due to accumulated non executed tuples when scaling is carried out. Likewise, when the spout's throughput is low enough our controller removes unnecessary instances.

Note that different components are scaled separately. Format has been the first to be scaled, and as it became able to process all the tuples its received, it emitted more tuples which resulted in Slice being overloaded later on. Slice's throughput is roughly half the throughput of the previous components as it filters only load related measurements which accounts for half the total reported measurements. Thanks to the elasticity, our framework has been able to efficiently support the variations of dataload.

Conclusion

This paper demonstrates the CIRUS project in the aspects of an Ubilytics novel generic architecture and its elastic implementation for a smart-grid use case. The CIRUS project aims to deliver a self-adaptive cloud-based framework for Ubilytics applications. The CIRUS framework collects and analyses IoT data for M2M services using COTS such as IoT gateways, Message brokers or Message-as-a-Service providers and BDA platforms. For the smart-grid use case, CIRUS deploys OpenHAB processes for house gateways, several MQTT brokers and a Storm topology which analyses the sensor data stream of the smart-grid application for power prediction. The experiment validated the genericity and elasticity of CIRUS's architecture.

Fig. 5 :

 5 Fig. 5: The Storm topology used in the smart-grid use case

Fig. 6 :

 6 Fig. 6: Throughput per component

Fig. 7 :

 7 Fig. 7: Parallelism per component

Now is ESH[START_REF] Esh | [END_REF]


Andre Matthias Müller

Payam Ansari

Nader Ale Ebrahim

PhD Selina Khoo

N Ale

Matthias André
email: andrematthiasmueller@gmail.com

M A Müller

activity-and-aging-research-a-bibliometric-analysis Physical activity and aging research: A bibliometric analysis

Keywords: aging, bibliometrics, citation analysis, citation patterns, exercise, health, older adults, scientific publications

come   L'archive ouverte pluridisciplinaire

The world is aging at a rapid pace and it is predicted that by the year 2050, about 2 billion people, accounting for 20% of the global population, will be 60 years or older (United Nations, Department of Economic and Social Affairs, 2012). One of the most important nonpharmaceutical ways towards healthy aging is physical activity (PA) (Chodzko-Zajko, Proctor, Singh, Salem, & Skinner, 2009;[START_REF] Södergren | Lifestyle predictors of healthy ageing in men[END_REF][START_REF] Vogel | Health benefits of physical activity in older patients: a review[END_REF]. One of the first journal articles describing the positive effects of PA on health was published in 1953, [START_REF] Morris | Coronary heart-disease and physical activity of work[END_REF], but it was only in the 1970s and 1980s that publications on PA and aging started to emerge [START_REF] Spirduso | Reaction and movement time as a function of age and physical activity level[END_REF][START_REF] Morris | Life quality changes of older adults during various physical-activity programs[END_REF]. For example, a study conducted by Morris and colleagues reported on the changes in quality of life in older adults who do PA [START_REF] Morris | Life quality changes of older adults during various physical-activity programs[END_REF]. Hence, PA and aging research has a relatively short history [START_REF] Berryman | Exercise is medicine: A historical perspective[END_REF]. Initially, PA and aging research mainly focused on the effects of PA (especially exercise) [START_REF] Paffenbarger | Physical activity, allcause mortality, and longevity of college alumni[END_REF][START_REF] Wagner | Effects of physical activity on health status in older adults I: Observational studies[END_REF] and PA predictors as well as barriers [START_REF] Duda | Predictors of personal investment in physical-activity among middle-ages and older adults[END_REF]. During the 1990s, research evolved and concentrated mainly on measuring PA in older adults [START_REF] Kuhn | Measurement of low-level physical-activity in older adults[END_REF][START_REF] Nichols | A validation of a physical activity monitor for young and older adults[END_REF]. The Physical Activity Scale for the Elderly (PASE) by [START_REF] Washburn | The Physical-Activity Scale for the Elderly (PASE) -Development and evaluation[END_REF] was the first valid and reliable instrument to measure PA in older adults. With the publication of PA guidelines for older adults (Nelson et al., 2007;World Health Organization, 2010) and the emerging evidence of the benefits of PA, recent studies have focused primarily on interventional research [START_REF] King | Interventions to promote physical activity by older adults[END_REF][START_REF] Chase | Interventions to increase physical activity among older adults: A metaanalysis[END_REF].

Although the field of PA and aging has produced a great number of publications, there have been few attempts to gather bibliometric data in a systematic manner to identify research trends and highlight publications which have impacted the development of the field.

There has only been one bibliometric study in a related field, namely, sport and exercise science. However, it merely focused on abstracts published in Research Quarterly for Exercise and Sport between 1992 and 2011 with no information on PA [START_REF] Cardinal | Leading contributors to the Research Consortium's Annual Program, 1992-2011: High-visibility institutions, researchers, and topics[END_REF].

Traditionally, the field of bibliometrics is concerned with the analysis of citation indices in order to identify highly cited authors, publications and the most productive countries as well as institutions. This information is crucial because it highlights those who drive the trend in a research field and provides information on what is topical [START_REF] Eshraghi | 100 topcited scientific papers in limb prosthetics[END_REF]. Bibliometric studies have been widely applied in a variety of other research areas and provided valuable insights [START_REF] Eshraghi | 100 topcited scientific papers in limb prosthetics[END_REF][START_REF] Ho | Classic articles on social work field in Social Science Citation Index: a bibliometric analysis[END_REF][START_REF] Tian | Global scientific production on GIS research by bibliometric analysis from 1997 to 2006[END_REF].

One of the most popular bibliometric measures is the citation index introduced by Garfield in 1955 [START_REF] Garfield | Citation indexes for science: A new dimension in documentation through association of ideas[END_REF]. The citation index traces references in a published article and shows how many times a publication has been cited by other publications [START_REF] Garfield | Citation indexing: Its theory and application in science[END_REF]. Although the citation frequency of a publication is a measure of its impact, it is not necessarily an indicator of quality [START_REF] Brandt | Citation classics in obstetrics and gynecology: the 100 most frequently cited journal articles in the last 50 years[END_REF]. Evidently, citation count alone is not sufficient in judging the quality of a scientific publication, particularly when there are numerous mechanisms to increase the citation count of a publication [START_REF] Bornmann | How to improve the prediction based on citation impact percentiles for years shortly after the publication date[END_REF].On the other hand, the citation count remains a main indicator of the significance of research output rather than alternative metrics [START_REF] Priem | Scholarship: Beyond the paper[END_REF][START_REF] Shotton | Open citations[END_REF]. However, by eliminating the effect of article lifetime that increases the opportunity of increasing citations, the citation per year metric reflects the quality of an article better than the total citation frequency [START_REF] Chuang | High-impact papers presented in the subject category of water resources in the essential science indicators database of the institute for scientific information[END_REF]. Recognizing that a pure analysis of citation quantity by author, institution and country is not sufficient to identify research trends, the current paper also attempts to analyze the content of PA and aging research by providing analysis of the topics covered in highly cited articles.

With this, the purpose of this paper is to bibliometrically analyze the publications registered in the Web of Science (WoS) Core Collection on PA and aging from 1980 until February 6, 2015 and to also provide an overview of the highly cited articles.

Methods

The bibliometric data for this paper were derived from WoS Core Collection citation indexes databases on February 6, 2015. Prior to data collection we circulated a survey to 20 researchers in the field of PA and/or aging. In this survey respondents were asked to indicate search terms they would use to retrieve publications on PA and aging. For the search category PA, we searched titles in published articles using the following search terms that were combined with the Boolean Operator "OR": "physical activit*", "exercise*", "walk*", "fitness", "aerobic", "active living", "active commuting", "physical fit*", "physical training", "sport*", "master athlete", "active transport*" and "leisure activit*". This search yielded 240,610 citations. We further refined the search by excluding the following search terms using the Boolean Operator "NOT": "adolescen*", "young", "youth", "school", "kid", "child*", "kindergarten" and "middle-aged". With this, we reduced the citations to 215,222.

In order to retrieve publications that specifically focused on older adults, we refined our search with the following search terms (in title, abstract or keywords of articles) that were combined with the Boolean Operator "OR": "older adult*", "pensioner*, "retiree*", "senior*", "elder*", "older person*", "older people", "aging", "aged", "mature adult*", "aging population*", and "geriatric*". This refinement led to 9,935 publications being included in the current research. Data of these articles were downloaded to Microsoft Excel. Highly cited publications were selected according to the Essential Science Indicators (ESI). This indicator highlights publications that are ranked within the top 1% of the citations list over the past 10 years [START_REF] Bornmann | A multilevel modelling approach to investigating the predictive validity of editorial decisions: Do the editors of a high profile journal select manuscripts that are highly cited after publication[END_REF]. To determine which papers are highly cited, an annual threshold per field is established in WoS.

For the overall publication sample, we explored the publication output over time and described some characteristics (e.g., publication category, institutions and authors with the highest publication output, and journals that published most articles). For highly cited publications, we calculated the citations per year by dividing the total citations of a paper (citation count) by the number of years since publication. We used this citation rate in our analysis. The citation rate is a more accurate and reliable measure of the impact of a research paper compared to the citations count because the latter depend on time of publication [START_REF] Chuang | High-impact papers presented in the subject category of water resources in the essential science indicators database of the institute for scientific information[END_REF][START_REF] Ho | Classic articles on social work field in Social Science Citation Index: a bibliometric analysis[END_REF]. From this analysis, we identified the top five papers in PA and aging research. We also described other characteristics of these highly cited papers such as journals publishing the highly cited papers and most prolific authors of highly cited papers.

Additionally, the publications were categorized based on type, namely cross-sectional, experimental, review/meta-analysis, longitudinal, position paper/recommendation, and validation/reliability studies. Finally, we categorized the publications according to study focus, e.g., associations/effects of PA with health related outcomes, PA levels, and PA promotion.

Results

Overall Trend

The systematic search identified 9,935 citations published between 1980 and February 6, 2015. The majority of published items were published in the English language (96%) and appeared as journal articles (55.5%). However, 33% of the publications were published as a meeting abstract. Figure 1 displays the steady increase of publications over time with the most items published in 2012 (n = 972). About one-third of the publications were in the Geriatrics and Gerontology category (30.1%), with a substantial number of publications in Sport Sciences (27.5%) and Psychology (7.6%) as the second and third most represented publication categories, respectively. Of the total 9,935 publications, over 35% (n = 3,489) did not receive any citations, whereas 7.8% (n = 771) publications received less than 10 citations and more than half of the publications (57.1%, n = 5,675) received more than 10 citations.

Highly Cited Publication

We obtained 45 highly cited papers from the search. Highly cited publications for the year 2014 were published between 2004 and 2014, with a citation rate between 5.50 and 78.13 (21.18 ± 17.01 citations per annum). Combined, these publications received 6,758 citations (2.92% of the overall citations received by all 9,935 publications).

A 2007 paper providing PA recommendations for older adults received the highest number of citations ( 625) and the highest number of citations per year/citation rate (78.13) (Nelson et al., 2007). Table 1 displays

Authors and countries of origin (corresponding authors). AC King contributed

most to the highly cited publications in the area of PA and aging research (6 publications) followed by 3 authors with 4 publications each. The corresponding authors of the 45 highly cited publications were from 11 countries, with authors based in the US responsible for most of the papers (n = 27) followed by authors based in Canada with 6 publications.

Research type and focus of research. Publications reporting on cross-sectional (n = 16), experimental (n = 14) and systematic review/meta-analysis studies (n = 9) made up 87% of the highly cited publications. None of the publications used a qualitative approach. In terms of research focus, most research explored associations and/or effects of PA, exercise and/or fitness on health-related outcomes (n = 27). The highly cited publications were also frequently concerned with factors associated with participation in PA and exercise (n = 10).

Table 3 displays the research fields covered by the highly cited papers.

Discussion

PA and aging research is a field that has recently attracted increased attention. The bibliometric analysis presented in this paper adds value to the existing body of literature because it provides an overview of the overall publication landscape and informs the scientific community about the publications that had the most impact on PA and aging research [START_REF] Chuang | High-impact papers presented in the subject category of water resources in the essential science indicators database of the institute for scientific information[END_REF].

The publication output increased steadily over time, with a steep increase from 2008 and peak in 2012 where almost 1,000 scholarly works were published. This development coincides with the increased scientific recognition of the various health benefits of a variety of PAs in older adulthood [START_REF] Keogh | Physical benefits of dancing for healthy older adults: a review[END_REF][START_REF] Paterson | Ageing and physical activity: evidence to develop exercise recommendations for older adults[END_REF][START_REF] Taylor | Physical activity and older adults: a review of health benefits and the effectiveness of interventions[END_REF] and the publication of two influential works providing detailed PA recommendations for older adults (Nelson et al., 2007;World Health Organization, 2010). Further, not only has the amount of publications greatly increased over time but researchers from various fields have entered the conversation allowing the field to mature. While the benefits of PA as well as the type, intensity, and frequency of PA are a primary concern of medical professionals (especially geriatricians and gerontologists) and sport scientists, health psychologists provide valuable insights on how to promote PA participation among older adults [START_REF] Hobbs | Are behavioral interventions effective in increasing physical activity at 12 to 36 months in adults aged 55 to 70 years? A systematic review and meta-analysis[END_REF][START_REF] Mcauley | Growth trajectories of exercise self-efficacy in older adults: influence of measures and initial status[END_REF][START_REF] Mcauley | Predicting long-term maintenance of physical activity in older adults[END_REF].

Although scholars from a number of different fields are contributing to the PA and aging research, there appears to be a regional dominance in terms of publication output with most publications coming from North American institutions, especially the US. The strong influence of US institutions in other fields of research has been previously reported [START_REF] Eshraghi | 100 topcited scientific papers in limb prosthetics[END_REF][START_REF] Shadgan | Top-cited articles in rehabilitation[END_REF]. This is mainly due to funding opportunities and a great number of well-trained scientists in the US. Furthermore, approximately one-third (33.5%, n = 2,894) of journals indexed in Thomson Reuters' WoS under the science edition are based in the US (as of July 2015). This gives scholars and institutions in the US more opportunities to publish as many of these journals published predominantly articles by US-based authors [START_REF] Campbell | National bias: a comparison of citation practices by health professionals[END_REF]. This is mirrored in our findings where US-based journals published the most articles and the authors with the highest publication output are attached to US institutions. However, research and publications from other regions of the world are needed. This is especially true for Asia where the rapidly growing elderly populations in many countries lack adequate PA [START_REF] Hallal | Global physical activity levels: surveillance progress, pitfalls, and prospects[END_REF][START_REF] Ibrahim | Perceived physical activity barriers related to body weight status and sociodemographic factors among Malaysian men in Klang Valley[END_REF]. Hence, it is vital to increase research that primarily focusses on promoting PA in various regions of the world. This is because current knowledge on ways to increase PA in older adults is based on research from developed countries which might not be applicable to developing countries.

Within the total of 9,935 publications in PA and aging research, the 45 highly-cited publications have greatly influenced the scientific community. All the highly cited publications were quantitative in nature and most were cross-sectional, experimental, and review or meta-analytic studies. This is not surprising because these study designs are frequently applied in health science and provide reliable information [START_REF] Higgins | Cochrane handbook for systematic reviews of interventions[END_REF]. However, qualitative research in the PA and aging field should not be neglected by scholars.

The majority of highly cited publications are concerned with the association or effect of PA on health-related outcomes in older adults. These studies provided the historical basis and justification for further PA and aging research and are therefore of invaluable importance to current scholars. For example, the early studies by Morris andcolleagues (1953, 1983) triggered the initial debate on the health benefits of PA, whereas current reports on PA and e.g., brain plasticity as well as mental health (Colcombe et al., 2006;Larson et al., 2006;Lautenschlager et al., 2008) identified PA as a valuable non-pharmaceutical way of dementia prevention. These publications on the effects of PA in older adults have traditionally paved the way for further research and are, therefore cited frequently. However, other studies are equally important and are gaining popularity. This is especially so for interventional studies that aim to increase PA in older adults [START_REF] Macera | State of the art review: Physical activity and older adults[END_REF][START_REF] Chase | Interventions to increase physical activity among older adults: A metaanalysis[END_REF][START_REF] Taylor | Physical activity and older adults: a review of health benefits and the effectiveness of interventions[END_REF]. Since research on the effects of PA has undoubtedly confirmed that PA is beneficial for older adults the current and future challenge lies in the design of The publication by Nelson et al. (2007) introducing PA recommendations for older adults was most highly cited. This article can be seen as one of the pillars of PA and aging research because it serves as a point of reference for many researchers in the field.

Although we conducted a methodologically sound bibliometric study, a number of limitations need to be acknowledged. First, our search for relevant publications was only conducted in WoS and hence, only publications registered in Thomson Reuters' WoS were included. Consequently, other potentially influential works such as reports and books were not captured. Second, we were not able to examine if every one of the 9,935 publications that we included were related to PA and aging research. However, considering our systematic search we assume that the vast majority of the publications matched our inclusion criteria.

Finally, we used highly cited publications as suggested by WoS and based on the citation rate to determine the most influential works in PA and aging research. This is problematic because the citation rate is only one measure of impact and may not always be an indicator of research excellence [START_REF] Brandt | Citation classics in obstetrics and gynecology: the 100 most frequently cited journal articles in the last 50 years[END_REF].

Conclusions

This paper presents the first attempt to conduct a bibliometric study in the field of PA and aging research. From the 9,935 publications included in this research, publication output across a variety of disciplines has steadily increased over time indicating the growing importance of the field. Most publications were authored by scholars from the US where most of the influential journals are located. Thus, the US appears to lead the scientific community in PA and aging research. However, it is expected that scholars from other fast aging regions will influence the research landscape in the future [START_REF] Cramm | Smoking, physical activity and healthy aging in India[END_REF].

Highly cited publications reported on quantitative studies and focused predominantly on the association or effects of PA on health-related outcomes. Interventional PA research focusing on the increase of activity levels is strongly encouraged.

Figure 1 .

 1 Figure 1. Publication output in PA and aging between 1980 and February 6, 2015.

  the top 5 publications in the field of PA and aging research based on the conducted search (the remaining 40 publications are listed in the supplementary material).

  effective and economically viable PA interventions. Using modern technology to deliver PA interventions might prove particularly promising considering the increased use of mobile phones and the Internet by older adults[START_REF] Müller | Non-face-to-face physical activity interventions in older adults: a systematic review[END_REF][START_REF] Gell | Patterns of technology use among older adults with and without disabilities[END_REF].

Table 1

 1 The Top five Publications in PA and Aging Research Recommendation from the American College of Sports Medicine and the American Heart Association.

	Times	Citation
			Title
	Cited	Rate
	625	78.13 Nelson, M. Medicine and Science in Sports and Exercise, 116(9), 1094-1105.
	456	76.0	Chodzko-Zajko W. J., Proctor, D. N., Singh, M. A. F., Salem, G. J., &
			Skinner, J. S. (2009). Position Stand. Exercise and physical activity for

E., Rejeski, W. J., Blair, S. N., Duncan, P. W., Judge, J. O.,

King, A. C., … & Castaneda-Sceppa, C. (2007)

. Physical activity and public health in older adults: B., Wang, L., Bowen, J. D., McCormick, W.C., Teri, L., Crane, P., & Kukull, W. (2006). Exercise is associated with reduced risk for incident dementia among persons 65 years of age and older. Annals of Internal Medicine, 144(2), 73-81. 408 45.33 Colcombe, S. J., Erickson, K. I., Scalf, P. E., Kim, J. S., Prakash, R.; McAuley, E., … & Kramer, A. F. (2006). Aerobic exercise training increases brain volume in aging humans. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61(11), 1166-1170.

  Table 2 displays the total citations received and the citation rate of the top 4 authors.

	Table 2
	The Top four Authors of the Highly Cited Publications
	Author's Name	Number of	Citations	Mean Citation rate 1
		Publications
	King, A. C.	6	1,041	24.65
	Pahor, M.	4	859	21.76
	Kritchevsky, S. B.	4	619	16.30
	Frank, L. D.	4	190	11.10

1 Mean citation rate per publication

Table 3

 3 Research Fields of the Highly Cited Publications

	Field	Papers	Mean Citation Rate 1
	Effects/Associations of PA/exercise/fitness with
		27	24.30
	health related outcomes
	Factors associated with PA participation	10	12.85
	PA measurement	3	9.86
	PA levels	2	6.25
	PA promotion (interventions)	1	25.44
	Recommendations for PA	1	78.13
	Effect of X on PA and fitness	1	22.50

1 Mean citation rate per publication

Table of the top 45 publications in Physical Activity and Aging Research Rank Citation Rate Title

Competing Interests

The authors declare that they have no competing interests.