Collaborative filtering on data streams

Collaborate Filtering is one of the most popular recommendation algorithms. Most Collaborative Filtering algorithms work with a static set of data. This paper introduces a novel approach to providing recommendations using Collaborative Filtering when user rating is received over an incoming data stream. In an incoming stream there are massive amounts of data arriving rapidly making it impossible to save all the records for later analysis. By dynamically building a decision tree for every item as data arrive, the incoming data stream is used effectively although an inevitable trade off between accuracy and amount of memory used is introduced. By adding a simple personalization step using a hierarchy of the items, it is possible to improve the predicted ratings made by each decision tree and generate recommendations in real-time. Empirical studies with the dynamically built decision trees show that the personalization step improves the overall predicted accuracy.

Matching commercial clips from TV streams using a unique, robust and compact signature

One of critical challenges in automatic recognition of TV commercials is to generate a unique, robust and compact signature. Uniqueness indicates the ability to identify the similarity among the commercial video clips which may have slight content variation. Robustness means the ability to match commercial video clips containing the same content but probably with different digitalization/encoding, some noise data, and/or transmission and recording distortion. Efficiency is about the capability of effectively matching commercial video sequences with a low computation cost and storage overhead. In this paper, we present a binary signature based method, which meets all the three criteria above, by combining the techniques of ordinal and color measurements. Experimental results on a real large commercial video database show that our novel approach delivers a significantly better performance comparing to the existing methods.

Filtering duplicate items over distributed data streams

In recent years many real time applications need to handle data streams. We consider the distributed environments in which remote data sources keep on collecting data from real world or from other data sources, and continuously push the data to a central stream processor. In these kinds of environments, significant communication is induced by the transmitting of rapid, high-volume and time-varying data streams. At the same time, the computing overhead at the central processor is also incurred. In this paper, we develop a novel filter approach, called DTFilter approach, for evaluating the windowed distinct queries in such a distributed system. DTFilter approach is based on the searching algorithm using a data structure of two height-balanced trees, and it avoids transmitting duplicate items in data streams, thus lots of network resources are saved. In addition, theoretical analysis of the time spent in performing the search, and of the amount of memory needed is provided. Extensive experiments also show that DTFilter approach owns high performance.

Build-up of the tendency to segregate auditory streams:resetting effects of single deviant tones

The tendency to hear a sequence of alternating low (L) and high (H) frequency tones as two streams can be increased by a preceding induction sequence, even one composed only of same-frequency tones. Four experiments used such an induction sequence (10 identical L tones) to promote segregation in a shorter test sequence comprising L and H tones. Previous studies have shown that the build-up of stream segregation is usually reduced greatly when a sudden change in acoustic properties distinguishes all of the induction tones from their test-sequence counterparts. Experiment 1 showed that a single deviant tone, created by altering the final inducer (in frequency, level, duration, or replacement with silence) reduced reported segregation, often substantially. Experiment 2 partially replicated this finding, using changes in temporal discrimination as a measure of streaming. Experiments 3 and 4 varied the size of a frequency change applied to the deviant tone; the extent of resetting varied with size only gradually. The results suggest that resetting begins to occur once the change is large enough to be noticeable. Since the prior inducers always remained unaltered in the deviant-tone conditions, it is proposed that a single change actively resets the build-up evoked by the induction sequence.

Interest groups in multiple streams:specifying their involvement in the framework

Although interests inhabit a central place in the multiple streams framework (MSF), interest groups have played only a minor role in theoretical and empirical studies until now. In Kingdon’s original conception, organized interests are a key variable in the politics stream. Revisiting Kingdon’s concept with a particular focus on interest groups and their activities—in different streams and at various levels—in the policy process, we take this argument further. In particular, we argue that specifying groups’ roles in other streams adds value to the explanatory power of the framework. To do this, we look at how interest groups affect problems, policies, and politics. The influence of interest groups within the streams is explained by linking the MSF with literature on interest intermediation. We show that depending on the number of conditions and their activity level, interest groups can be involved in all three streams. We illustrate this in case studies reviewing labor market policies in Germany and chemicals regulation at the European level.

Detecting EPCIS exceptions in linked traceability streams across supply chain business processes

The EPCIS specification provides an event oriented mechanism to record product movement information across stakeholders in supply chain business processes. Besides enabling the sharing of event-based traceability datasets, track and trace implementations must also be equipped with the capabilities to validate integrity constraints and detect runtime exceptions without compromising the time-to-deliver schedule of the shipping and receiving parties. In this paper we present a methodology for detecting exceptions arising during the processing of EPCIS event datasets. We propose an extension to the EEM ontology for modelling EPCIS exceptions and show how runtime exceptions can be detected and reported. We exemplify and evaluate our approach on an abstraction of pharmaceutical supply chains.