Journal of Innovation in Digital Ecosystems最新文献

Rogue behaviors refer to behavioral anomalies that can occur in human activities and that can thus be retrieved from human generated data. In this paper, we aim at showing that NoSQL graph databases are a useful tool for this purpose. Indeed these database engines exploit property graphs that can easily represent human and object interactions whatever the volume and complexity of the data. These interactions lead to fraud rings in the graphs in the form of sophisticated chains of indirect links between fraudsters representing successive transactions (money, communications, etc.) from which rogue behaviours are detected. Our work is based on two extensions of such NoSQL graph databases. The first extension allows the handling of time-variant data while the second one is devoted to the management of imprecise queries with a DSL (to define flexible operators and operations with Scala) and the Cypherf declarative flexible query language over NoSQL graph databases. These extensions allow to better address and describe sophisticated frauds. Feasibility have been studied to assess our proposition.

In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as local process models. Local process model mining can be positioned in-between process discovery and episode/sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode/sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.

Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work, we investigate whether tags accompanying photos in the Instagram can be considered as image annotation metadata. If such a claim is proved then Instagram could be used as a very rich, easy to collect automatically, source of training data for the development of AIA techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner/creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose 1000 Instagram images were collected and one to four hashtags, considered as the most descriptive ones for the image in question, were chosen among the hashtags used by the photo owner. An online database was constructed to generate online questionnaires containing 20 images each, which were distributed to experiment participants so they can choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.

In this paper, we focus on the building performance assessment using big data and visual analytics techniques driven by building occupancy. Building occupancy is a paramount factor in building performance, specifically lighting, plug loads and HVAC equipment utilization. Extrapolation of patterns from big data sets, which consist of building information, energy consumption, environmental measurements and namely occupancy information, is a powerful analysis technique to extract useful semantic information about building performance. To this end, visual analytics techniques are exploited to visualize them in a compact and comprehensive way taking into account properties of human cognition, perception and sense making. Visual Analytics facilitates the detailed spatiotemporal analysis building performance in terms of occupancy comfort, building performance and energy consumption and exploits innovative data mining techniques and mechanisms to allow analysts to detect patterns and crucial point that are difficult to be detected otherwise, thus assisting them to further optimize the building’s operation. The presented tool has been tested on real data information acquired from a building located at southern Europe demonstrating its effectiveness and its usability for building managers.

Probing Environment and Adaptive Sleeping (PEAS) is one of the most cited protocols in the literature for coverage in Wireless Sensor Networks (WSNs). PEAS maintains only two variables: the number of received messages N and the period of time T necessary to receive these messages. Sensor nodes do not keep any information about their neighbors. In this paper we present PEAS-LI an extension of PEAS to improve the coverage and connectivity. PEAS-LI operates in two steps, initially we apply PEAS as described in Ye et al. (2003) then the neighbors exchange their state and location information in order to estimate precisely the coverage and to make their decision basing on the gathered information. The alone additional requirement is that PEAS-LI supposes that each node knows its position in the monitored area of interest (AI). PEAS-LI performance evaluation proves that it is a robust protocol with high coverage ratio and that it outperforms PEAS and a set of other protocols.

This paper aims to address the assessment the similarity between documents or pieces of documents. For this purpose we have developed CASISS (CAlculation of SImilarity of Semi-Structured documents) method to quantify how two given texts are similar. The method can be employed in wide area of applications including content reuse detection which is a hot and challenging topic. It can be also used to increase the accuracy of the information retrieval process by taking into account not only the presence of query terms in the given document (Content Only search — CO) but also the topology (position continuity) of these terms (based on Content And Structure Search — CAS). Tracking the origin of the information in social media, copy right management, plagiarism detection, social media mining and monitoring, digital forensic are among other applications require tools such as CASISS to measure, with a high accuracy, the content overlap between two documents.

CASISS identify elements of semi-structured documents using elements descriptors. Each semi-structured document is pre-processed before the extraction of a set of elements descriptors, which characterize the content of the elements.

This paper presents meaning-based machine learning, the use of semantically meaningful input data into machine learning systems in order to produce output that is meaningful to a human user where the semantic input comes from the Ontological Semantics Technology theory of natural language processing. How to bridge from knowledge-based natural language processing architectures to traditional machine learning systems is described to include high-level descriptions of the steps taken. These meaning-based machine learning systems are then applied to problems in information assurance and security that remain unsolved and feature large amounts of natural language text.

Being a widely adapted and acknowledged practice for the representation of inter- and intra-dependent information streams, network graphs are nowadays growing vast in both size and complexity, due to the rapid expansion of sources, types, and amounts of produced data. In this context, the efficient processing of the big amounts of information, also known as Big Data forms a major challenge for both the research community and a wide variety of industrial sectors, involving security, health and financial applications. Serving these emerging needs, the current paper presents a Graph Analytics based Platform (GAP) that implements a top-down approach for the facilitation of Data Mining processes through the incorporation of state-of-the-art techniques, like behavioural clustering, interactive visualizations, multi-objective optimization, etc. The applicability of this platform is validated on 2 istinct real-world use cases, which can be considered as characteristic examples of modern Big Data problems, due to the vast amount of information they deal with. In particular, (i) the root cause analysis of a Denial of Service attack in the network of a mobile operator and (ii) the early detection of an emerging event or a hot topic in social media communities. In order to address the large volume of the data, the proposed application starts with an aggregated overview of the whole network and allows the operator to gradually focus on smaller sets of data, using different levels of abstraction. The proposed platform offers differentiation between different user behaviors that enable the analyst to obtain insight on the network’s operation and to extract the meaningful information in an effortless manner. Dynamic hypothesis formulation techniques exploited by graph traversing and pattern mining, enable the analyst to set concrete network-related hypotheses, and validate or reject them accordingly.

Managing risks in large information infrastructures is often tied to inevitable simplification of the system, to make a risk analysis feasible. One common way of “compacting” matters for efficient decision making is to aggregate vulnerabilities and risks identified for distinct components into an overall risk measure related to an entire subsystem and the system as a whole. Traditionally, this aggregation is done pessimistically by taking the overall risk as the maximum of all individual risks, following the heuristic understanding that the “security chain” is only as strong as its weakest link. As that method is quite wasteful of information, this work proposes a new approach, which uses neural networks to resemble human expert’s decision making in the same regard. To validate the concept, we conducted an empirical study on human expert’s risk assessments, and trained several candidate networks on the empirical data to identify the best approximation to the opinions in our expert group.

Software Ecosystem (SECO) is often understood as a set of actors interacting among themselves and manipulating artifacts with the support of a common technology platform. Usually, SECO approaches can be designed as an environment whose component repository is gathering stakeholders as well as software products and components. By manipulating software artifacts, a technical network emerges from interactions made over the component repository in order to reuse artifacts, improving code quality, downloading, selling, buying etc. Although technical repositories are essential to store SECO’s artifacts, the interaction among actors in an emerging social network is a key factor to strengthen the SECO’s through increasing actor’s participation, e.g., developing new software, reporting bugs, and communicating with suppliers. In the SECO context, both the internal and external actors keep the platform’s components updated and documented, and even support requirements and suggestions for new releases and bug fixes. However, those repositories often lack resources to support actors’ relationships and consequently to improve the reuse processes by stimulating actors’ interactions, information exchange and better understanding on how artifacts are manipulated by actors. In this paper, we focused on investigating SECO as component repositories that include socio-technical resources. As such, we present a survey that allowed us to identify the relevance of each resource for a SECO based on component repositories, initially focused on the Brazilian scenario. This paper also describes the analysis of the data collected in that survey. Information of other SECO elements extracted from the data is also presented, e.g., the participants’ profile and how they behave within a SECO. As an evolution of our research, a study for evaluating the availability and the use of such resources on top of two platforms was also conducted with experts in collaborative development in order to analyze the usage of the most relevant resources in real SECO’s platforms. We concluded that socio-technical resources have aided collaboration in software development for SECO, coordination of teams based on more knowledge of actor’s tasks and interactions, and monitoring of quality of SECOs’ platforms through the orchestration of the contributions developed by external actors.