Recent technological advances in sensor networks and mobile devices give rise to new challenges in processing of live streams. In particular, time-series sequence matching, namely, the similarity matching of live streams against a set of predefined pattern sequence queries, is an important technology for a broad range of domains that include monitoring the spread of hazardous waste and administering network traffic. In this thesis, I use the time critical application of monitoring of fire growth in an intelligent building as my motivating example. Various measures and algorithms have been established in the current literature for similarity of static time-series data. Matching continuous data poses the following new challenges: 1) fluctuations in stream characteristics, 2) real-time requirements of the application, 3) limited system resources, and, 4) noisy data. Thus the matching techniques proposed for static time-series are mostly not applicable for live stream matching. In this thesis, I propose a new generic framework, henceforth referred to as the n-Snippet Indices Framework (in short, SNIF), for discovering the similarity between a live stream and pattern sequences. The framework is composed of two key phases: (1.) Off-line preprocessing phase: where the pattern sequences are processed offline and stored into an approximate 2-level index structure; and (2.) On-line live stream matching phase: streaming time-series (or the live stream) is on-the-fly matched against the indexed pattern sequences. I introduce the concept of n-Snippets for numeric data as the unit for matching. The insight is to match small snippets of the live stream against prefixes of the patterns and maintain them in succession. Longer the pattern prefixes identified to be similar to the live stream, better the confirmation of the match. Thus, the live stream matching is performed in two levels of matching: bag matching for matching snippets and order checking for maintaining the lengths of the match. I propose four variations of matching algorithms that allow the user the capability to choose between the two conflicting characteristics of result accuracy versus response time. The effectiveness of SNIF to detect patterns has been thoroughly tested through extensive experimental evaluations using the continuous query engine CAPE as platform. The evaluations made use of real datasets from multiple domains, including fire monitoring, chlorine monitoring and sensor networks. Moreover, SNIF is demonstrated to be tolerant to noisy datasets.

Realtime transferring of data streams enables many data analytics and machine learning applications in the areas of e.g. massive IoT and industrial automation. Big data volume of those streams is a significant burden or overhead not only to the transportation network, but also to the corresponding application servers. Therefore, researchers and scientists focus on reducing the amount of data needed to be transferred via data compressions and approximations. Data compression techniques like lossy compression can significantly reduce data volume with the price of data information loss. Meanwhile, how to do data compression is highly dependent on the corresponding applications. However, when apply the decompressed data in some data analysis application like machine learning, the results may be affected due to the information loss. In this paper, the author did a study on the impact of data compression to the machine learning applications. In particular, from the experimental perspective, it shows the tradeoff among the approximation error bound, compression ratio and the prediction accuracy of multiple machine learning methods. The author believes that, with proper choice, data compression can dramatically reduce the amount of data transferred with limited impact on the machine learning applications. / Realtidsöverföring av dataströmmar möjliggör många dataanalyser och maskininlärningsapplikationer inom områdena t.ex. massiv IoT och industriell automatisering. Stor datavolym för dessa strömmar är en betydande börda eller omkostnad inte bara för transportnätet utan också för motsvarande applikationsservrar. Därför fokuserar forskare och forskare om att minska mängden data som behövs för att överföras via datakomprimeringar och approximationer. Datakomprimeringstekniker som förlustkomprimering kan minska datavolymen betydligt med priset för datainformation. Samtidigt är datakomprimering mycket beroende av motsvarande applikationer. Men när du använder dekomprimerade data i en viss dataanalysapplikation som maskininlärning, kan resultaten påverkas på grund av informationsförlusten. I denna artikel gjorde författaren en studie om effekterna av datakomprimering på maskininlärningsapplikationerna. I synnerhet, från det experimentella perspektivet, visar det avvägningen mellan tillnärmningsfelbundet, kompressionsförhållande och förutsägbarhetsnoggrannheten för flera maskininlärningsmetoder. Författaren anser att datakomprimering med rätt val dramatiskt kan minska mängden data som överförs med begränsad inverkan på maskininlärningsapplikationerna.