Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Connecting Web Event Forecasting with Anomaly Detection: A Case Study on
  Enterprise Web Applications Using Self-Supervised Neural Networks   Recently web applications have been widely used in enterprises to assist
employees in providing effective and efficient business processes. Forecasting
upcoming web events in enterprise web applications can be beneficial in many
ways, such as efficient caching and recommendation. In this paper, we present a
web event forecasting approach, DeepEvent, in enterprise web applications for
better anomaly detection. DeepEvent includes three key features: web-specific
neural networks to take into account the characteristics of sequential web
events, self-supervised learning techniques to overcome the scarcity of labeled
data, and sequence embedding techniques to integrate contextual events and
capture dependencies among web events. We evaluate DeepEvent on web events
collected from six real-world enterprise web applications. Our experimental
results demonstrate that DeepEvent is effective in forecasting sequential web
events and detecting web based anomalies. DeepEvent provides a context-based
system for researchers and practitioners to better forecast web events with
situational awareness.

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The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci