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27	27	Summing up, a #mok() system should be seen as a network of shared information repositories, in which some source entities continuously and spontaneously put data chunks.
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29		-Such data may then aggregate so as to reify some (potentially) relevant "knowledge-related patterns" — e.g. linking two news stories talking about the same person or written by the same author, read by the same prosumer or both related to a third news story — and (ii) diffuse among these networked shared spaces toward the (potentially) interested users — e.g. papers about MAS should strive to reach MAS researchers' repositories.
	29	+Such data may then aggregate so as to reify some (potentially) relevant "knowledge-related patterns" – e.g. linking two news stories talking about the same person or written by the same author, read by the same prosumer or both related to a third news story – and (ii) diffuse among these networked shared spaces toward the (potentially) interested users—e.g. papers about MAS should strive to reach MAS researchers' repositories.
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31		-Users can interact with the system through epistemic actions — e.g. read a post, contribute to a wiki, highlight words in an article, ... — which are tracked and exploited by the #mok() system to influence knowledge evolution transparently to the user — e.g., a user highlighting a given word may imply such user being highly interested in such topics, thus #mok() can react by, e.g., increasing rank position of related topics in a search query.
	31	+Users can interact with the system through epistemic actions – e.g. read a post, contribute to a wiki, highlight words in an article, ... – which are tracked and exploited by the #mok() system to influence knowledge evolution transparently to the user—e.g., a user highlighting a given word may imply such user being highly interested in such topics, thus #mok() can react by, e.g., increasing rank position of related topics in a search query.
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33	33	=== Motivation & Context ===
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35		-//Knowledge-intensive environments// and //socio-technical systems// are systems combining business processes, technologies and people's skills to store, handle, make accessible — in one word, manage — very large repositories of information — e.g. wiki portals, online press, enterprise social networks, etc.
	35	+//Knowledge-intensive environments// and //socio-technical systems// are systems combining business processes, technologies and people's skills to store, handle, make accessible – in one word, manage – very large repositories of information—e.g. wiki portals, online press, enterprise social networks, etc.
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37	37	They pose peculiar challenges from the infrastructural standpoint:
38		- * data size — from GBs to TBs
39		- * scale — from organization-wide to world-wide
40		- * dynamism — new information produced/consumed at fast pace — e.g. tweets
41		- * diversity — both in information representation and usage destination openness — new users can enter/leave the system at any time
42		- * unpredictability — since they involve humans, whose behaviour is rarely fully predictable
	38	+ * data size—from GBs to TBs
	39	+ * scale—from organization-wide to world-wide
	40	+ * dynamism—new information produced/consumed at fast pace — e.g. tweets
	41	+ * diversity—both in information representation and usage destination openness — new users can enter/leave the system at any time
	42	+ * unpredictability—since they involve humans, whose behaviour is rarely fully predictable
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44		-These challenges are usually faced using //brute force// approaches relying on ever-increasing (hopefully, endless) computational power and (ii) storage — "big data" techniques, non-relational large-scale DBs, "data-in-the-cloud" paradigm, other buzzwords.
	44	+These challenges are usually faced using //brute force// approaches relying on ever-increasing (hopefully, endless) computational power and (ii) storage— "big data" techniques, non-relational large-scale DBs, "data-in-the-cloud" paradigm, other buzzwords.
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46		-//This won't scale forever// — e.g. what about the end of Moore's law?
	46	+//This won't scale forever//—e.g. what about the end of Moore's law?
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48	48	One possible research line departs from the following question: why do we stick to view data as passive, "dead" things to run algorithms upon in the traditional I/O paradigm?
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