Pattern management is an important task in data stream mining and has attracted increasing attention recently. Variations of data stream patterns typically imply some fundamental changes of underlying objects and possess significant domain meanings. Many database applications require investigating the history information to get the knowledge about the evolving process of data streams. However, in most circumstances, the data stream patterns are unstructured: limited memory space cannot record all the patterns discovered online, no training sets or predefined models are available, and large numbers of noises bring another non-trivial challenge. This paper presents our research effort in online pattern management over such streams. A novel algorithm is proposed to detect stream changes, organize meaningful patterns and distinguish useful variations from noises. It extracts new trends from unstructured data heuristically, and involves a special parameter to identify whether the current event should be treated as significant. Several experiments are performed and the results prove this new method feasible and efficient.

Widely applied in many domains, data stream processing has attracted more and more attention in database and sensor network communities. In this demo, we present our system PEDS-VM, a real-time surveillance system for managing pattern variation over evolving medical streams. PEDS-VM utilizes an effective strategy and a state-based window framework to extract the evolving patterns. After extraction, PEDS-VM uses a novel storage structure called PGG to efficiently record these incremental patterns. Moreover, several important application scenarios of PEDS-VM are also discussed in this demonstration.

Many database applications require the analysis and processing of data streams. In such systems, huge amounts of data arrive rapidly and their values change over time. The variations on streams typically imply some fundamental changes of the underlying objects and possess significant domain meanings. In some data streams, successive events seem to recur in a certain time interval, but the data indeed evolves with tiny differences as time elapses. This feature is called pseudo periodicity, which poses a non-trivial challenge to variation management in data streams. This paper presents our research effort in online variation management over such streams, and the idea can be applied to the problem domain of medical applications, such as patient vital signal monitoring. We propose a new method named Pattern Growth Graph (PGG) to detect and manage variations over pseudo periodical streams. PGG adopts the wave-pattern to capture the major information of data evolution and represent them compactly. With the help of wave-pattern matching algorithm, PGG detects the stream variations in a single pass over the stream data. PGG only stores the different segments of the pattern for incoming stream, and hence it can substantially compress the data without losing important information. The statistical information of PGG helps to distinguish meaningful data changes from noise and to reconstruct the stream with acceptable accuracy. Extensive experiments on real datasets containing millions of data items demonstrate the feasibility and effectiveness of the proposed scheme.