Sensemaking is a common activity in the analysis of a large or complex amount of information. This active area of HCI research asks how DO people come to understand such difficult sets of information? The information workplace is... more
Sensemaking is a common activity in the analysis of a large or complex amount of information. This active area of HCI research asks how DO people come to understand such difficult sets of information? The information workplace is increasing dominated by high velocity, high volume, complex information streams. At the same time, understanding how sensemaking operates has become an urgent need in an era of increasingly unreliable news and information sources. While there has been a huge amount of work in this space, the research involved is scattered over a number of different domains with differing approaches. This workshop will focus on the most recent work in sensemaking, the activities, technologies and behaviors that people do when making sense of their complex information spaces. In the second part of the workshop we will work to synthesize a cross-disciplinary view of how sensemaking works in people, along with the human behaviors, biases, proclivities, and technologies required to support it.
In recent years, the number of attacks on the computer network is voluminous. Secure data communication over the network is always under threat of intrusions. To protect from these attacks various intrusion detection techniques have been... more
In recent years, the number of attacks on the computer network is voluminous. Secure data communication over the network is always under threat of intrusions. To protect from these attacks various intrusion detection techniques have been developed. Anomaly detection system detects the novel attacks based on deviation of the behavior of packets from the normal flow and Signature detection system detects known attacks based on stored signatures. We have proposed a Distributed collaboration detection scheme that combines the advantages of Anomaly and Signature based method by capturing the packets in real time. The uninteresting traffics are filtered by packet filtering and further normalization. The relevant features are selected based on our Correlation based BAT Feature Selection (CBBFS) Algorithm. Our Proposed Efficient Behavioral Prediction (EBP) scheme analyzes the episodes and classifies the attack based on EGSSI. Then Proficient Ordinance Generation (POG) for Inspection of IP Phase labels the IP as trusted or untrusted. Our proposed framework outperforms the results of existing classification algorithms (C4.5, Naive Bayes, PSO, GSA and EDADT) by reducing the rate of false positives.
The opportunities and need for interorganizational collaboration in all production sectors have increased markedly over the later part of last century, yet, despite an extensive interorganizational relationships literature, little is... more
The opportunities and need for interorganizational collaboration in all production sectors have increased markedly over the later part of last century, yet, despite an extensive interorganizational relationships literature, little is still known about the way work is achieved in interorganizational collaborations and teams. Studies have failed to reveal the essence of the collaborative interfirm dynamic; the relationships between operational-level employees and how these are manifested and sustained at an interactional level. This case study of interfirm collaboration in the furniture manufacturing sector in France addresses this gap in the literature by examining the communication processes between the operational-level employees who produce enduring patterns of engagement. The findings reveal how actions are coordinated at the micro level through conversations and, in so doing, clarify how collective competence is formed and becomes stable. The result is a unique contribution to the literatures on interorganizational relationships (IOR) and organizational communication.
