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Modern Economy (ME) is an international journal dedicated to the latest advancement of all areas of international economics. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of modern economy.

Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI+ for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI+.

Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.

The main characteristic to be aware of in these tools is that BE is primarily rule-based (using an embedded rule engine), whereas BW and iProcess are orchestration / flow engines. In BE we can use a state diagram to indicate a sequence of states which may define what process / rules apply, but this is really just another way of specifying a particular type of rules (i.e. state transition rules). The main advantages to specifying behavior as declarative rules are: Handling complex, event-driven behavior and choreography Iterative development, rule-by-rule The main advantages of flow diagrams and BPMN-type models are: Ease of understanding (especially for simpler process routes)  Process paths are pre-determined and therefore deemed guaranteeable. In combination these tools provide many of the IT capabilities required in an organization. For example, a business automation task uses BW to consolidate information from multiple existing sources, with human business processes for tasks such as process exceptions managed by iProcess. BE is used to consolidate (complex) events from systems to provide business information, or feed into or drive both BW and iProcess, and also monitors end-to-end system and case performance.

On Event Processing Agents implies  a “new” event processing reference architecture with terms like,   (1) simple event processing agents for filtering and routing, (2)  mediated event processing agents for event enrichment, transformation, validation, (3) complex event processing agents for pattern detection, and (4) intelligent event processing agents for prediction, decisions. Frankly, while I generally agree with the concepts, I think the terms in On Event Processing Agents tend to add to the confusion because these concepts in On Event Processing Agents are following, almost exactly, the same reference architecture (and terms) for MSDF, illustrated again below to aid the reader.

It is from this operational asymmetry that complexity in event processing is required.   In other words, as distributed networks grow in complexity, it is difficult to determine causal dependence when trying to diagnosis a distributed networked system.   Most who work in a large distributed network ecosystem (cyberspace) understand this.  The CEP notion of “the event cloud” was an attempt to express this complexity and uncertainly (in cyberspace).

JT has posted his view on rules and decisions and how they relate. Given that James talks more about services than events, I thought it would be worth reviewing his post from both a Complex Event Processing and a TIBCO BusinessEvents event processing platform perspective. ”Decision Services: Support business processes by making the business decisions that allow a process to continue. Support event processing systems by adding business decisions to event correlation decisions (they are often called Decision Agents in this context). Allow crucial and high-maintenance parts of legacy enterprise applications to be externalized for reuse and agility. Can be plugged into a variety of systems using Enterprise Service Bus approaches.”

Some definitions key on the question of the probability of encountering a given condition of a system once characteristics of the system are specified. Warren Weaver has posited that the complexity of a particular system is the degree of difficulty in predicting the properties of the system if the properties of the system’s parts are given. In Weaver’s view, complexity comes in two forms: disorganized complexity, and organized complexity. [2] Weaver’s paper has influenced contemporary thinking about complexity. [3]