Nowadays detecting opinion spam has attracted a lot of attention. Different approaches have been taken to tackle this problem. However, most of these approaches are neither domain-independent nor scalable. In this paper, we focus on the similar but more general problem on how to detect abnormal patterns in reviews. These abnormal patterns can indicate potential spam activities. We will implement a scalable, domain-independent framework with the help of the Hadoop ecosystem tools. We will extract certain relationships between different fields in our data and the rating that a review gave a product or service, and in turn determine how “abnormal” these relationships are based on statistics acquired from the entire dataset. We applied this technique to three datasets in different domains and found such abnormal patterns, which indicate potential spammers and/or spam activities.

The 13 papers in this special issue provide deep research results to report the advance of Big Data Analytics, Infrastructure, and Applications.

One of the main driving forces of the growing adoption of virtualization is its dramatic simplification of the provisioning and dynamic management of IT resources. By decoupling running entities from the underlying physical resources, and by providing easy-to-use controls to allocate, deallocate and migrate virtual machines (VMs) across physical boundaries, virtualization opens up new opportunities for improving overall system resource use and power efficiency. While a range of techniques for dynamic, distributed resource management of virtualized systems have been proposed and have seen their widespread adoption in enterprise systems, similar techniques for dynamic power management have seen limited acceptance. The main barrier to dynamic, power-aware virtualization management stems not from the limitations of virtualization, but rather from the underlying physical systems; and in particular, the high latency and energy cost of power state change actions suited for virtualization power management.

We describe an inexpensive autonomous robot capable of navigating previously unseen data centers and monitoring key metrics such as air temperature. The robot provides real-time navigation and sensor data to commercial IBM software, thereby enabling real-time generation of the data center layout, a thermal map and other visualizations of energy dynamics. Once it has mapped a data center, the robot can efficiently monitor it for hot spots and other anomalies using intelligent sampling. We demonstrate the robot's effectiveness via experimental studies from two production data centers.

In this paper, we describe results of a case study to establish the feasibility of deriving mappings between an abstract user level specification and the code elements in a concrete implementation of a highly secure smart card operating system. Such a mapping is necessary for feedback-directed specification-based test generation to improve code coverage, needed by the stringent criteria for high-assurance systems. We used test cases generated from the user level specification to identify the executed code elements and attempted to use static analysis to map the unexecuted code elements to the corresponding elements in the user level specification. Our primary result is evidence that, given a sufficiently expressive user level specification and a test generation system that is able to effectively use such a specification, the resulting tests will cover the vast majority of the code branches that are able to be covered. Therefore, the benefit of a feedback-directed system will be limited. We further provide evidence that the static analysis required to generate feedback in these cases tends to be difficult, involving inferring the semantics of the internal implementation of data structures. In particular, we observed that the internal states at the implementation level in a high security application pose significant challenges to this mapping process.